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第1行： − 已由Dorr初步翻译 − − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。{{short description|Social structure made up of a set of social actors}} − − − − {{about|the theoretical concept as used in the social and behavioral sciences|social networking sites|Social networking service|the 2010 movie|The Social Network|other uses|Social network (disambiguation)}} − − − − {{Sociology}} − − − 第19行：
第5行： − A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.+ − '''<font color="#ff8000">社会网络 Social Network</font>'''是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。对这些结构的研究使用'''<font color="#ff8000">社会网络分析 Social Network Analysis</font>'''以确定本地及全球模式，定位有影响力的实体，并审查网络动态。+ + − Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref>{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1"/><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref>{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref> − Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology, sociology, statistics, and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations". Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s. Social network analysis is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other complex networks, it forms part of the nascent field of network science. − 社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。'''格奥尔格·齐美尔 Georg Simmel'''是早期社会学结构主义理论的作者，他强调三合会和“群体关系网”的动态性。'''雅各布·莫雷诺 Jacob Moreno'''被认为是在20世纪30年代发展了第一份'''<font color="#ff8000">社交关系图 Sociogram</font>'''以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。社会网络分析现在是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。+ − + − − − ==Overview 概览== − − − − − Barabási model.]] − − − The social network is a theoretical construct useful in the social sciences to study relationships between individuals, groups, organizations, or even entire societies (social units, see differentiation). The term is used to describe a social structure determined by such interactions. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An axiom of the social network approach to understanding social interaction is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that individual agency is often ignored although this may not be the case in practice (see agent-based modeling). Precisely because many different types of relations, singular or in combination, form these network configurations, network analytics are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, sociology, and sociolinguistics.+ − 社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于'''<font color="#ff8000">人类学 Anthropology</font>'''、生物学、'''<font color="#ff8000">传播学 Communication Studies</font>'''、经济学、地理学、信息科学、组织学、社会心理学、社会学和'''<font color="#ff8000">社会语言学 Sociolinguistics</font>'''。 第59行：
第33行： − + + + − In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref>Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref>Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref>Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref> − − In the late 1890s, both Émile Durkheim and Ferdinand Tönnies foreshadowed the idea of social networks in their theories and research of social groups. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (Gemeinschaft, German, commonly translated as "community") or impersonal, formal, and instrumental social links (Gesellschaft, German, commonly translated as "society"). Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. Georg Simmel, writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups. − − 在19世纪90年代晚期，'''埃米尔·涂尔干 Émile Durkheim'''和'''斐迪南·滕尼斯 Ferdinand Tönnies'''在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。 第75行：
第46行： − + − − − − − − Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently. In psychology, in the 1930s, Jacob L. Moreno began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see sociometry). In anthropology, the foundation for social network theory is the theoretical and ethnographic work of Bronislaw Malinowski, Alfred Radcliffe-Brown, and Claude Lévi-Strauss. A group of social anthropologists associated with Max Gluckman and the Manchester School, including John A. Barnes, J. Clyde Mitchell and Elizabeth Bott Spillius, often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom. In sociology, the early (1930s) work of Talcott Parsons set the stage for taking a relational approach to understanding social structure. Later, drawing upon Parsons' theory, the work of sociologist Peter Blau provides a strong impetus for analyzing the relational ties of social units with his work on social exchange theory. 第90行：
第55行： − − By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist Harrison White and his students at the Harvard University Department of Social Relations. Also independently active in the Harvard Social Relations department at the time were Charles Tilly, who focused on networks in political and community sociology and social movements, and Stanley Milgram, who developed the "six degrees of separation" thesis. Mark Granovetter and Barry Wellman are among the former students of White who elaborated and championed the analysis of social networks. 第100行：
第63行： − − Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as Duncan J. Watts, Albert-László Barabási, Peter Bearman, Nicholas A. Christakis, James H. Fowler, and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks. 第113行：
第74行： − + − + − Centrality+ − − 中心性 − − In general, social networks are self-organizing, emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system. These patterns become more apparent as network size increases. However, a global network analysis of, for example, all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis. The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although levels of analysis are not necessarily mutually exclusive, there are three general levels into which networks may fall: micro-level, meso-level, and macro-level. 第137行：
第94行： − − At the micro-level, social network research typically begins with an individual, snowballing as social relationships are traced, or may begin with a small group of individuals in a particular social context. 第147行：
第102行： − − Dyadic level: A dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship (e.g. multiplexity, strength), social equality, and tendencies toward reciprocity/mutuality. 第157行：
第110行： − − Triadic level: Add one individual to a dyad, and you have a triad. Research at this level may concentrate on factors such as balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality. In the balance theory of Fritz Heider the triad is the key to social dynamics. The discord in a rivalrous love triangle is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of signed graphs. 第167行：
第118行： − − Actor level: The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, centrality, prestige and roles such as isolates, liaisons, and bridges. Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals. 第177行：
第126行： − − Subset level: Subset levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group actions or behavior. 第191行：
第138行： − − In general, meso-level theories begin with a population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks. 第200行：
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第154行： − − Organizations: Formal organizations are social groups that distribute tasks for a collective goal. Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures. 第219行：
第162行： − − Randomly distributed networks: Exponential random graph models of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general degree-based structural effects commonly observed in many human social networks as well as reciprocity and transitivity, and at the node-level, homophily and attribute-based activity and popularity effects, as derived from explicit hypotheses about dependencies among network ties. Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior. 第237行：
第178行： − − Scale-free networks: A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. In network theory a scale-free ideal network is a random network with a degree distribution that unravels the size distribution of social groups. Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of vertices with a degree that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This distribution also follows a power law. The Barabási model of network evolution shown above is an example of a scale-free network. 第251行：
第190行： − − Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population. 第260行：
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第206行： − − Large-scale networks: Large-scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics. Originally, the term was used extensively in the computer sciences (see large-scale network mapping). 第279行：
第214行： − − Complex networks: Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks. Such complex network features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure (see stochastic block model), and hierarchical structure. In the case of agency-directed networks these features also include reciprocity, triad significance profile (TSP, see network motif), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features. 第289行：
第222行： − + − − − − − − − − − − − Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are Graph theory, Balance theory, Social comparison theory, and more recently, the Social identity approach. − − − − − − − − Few complete theories have been produced from social network analysis. Two that have are structural role theory and heterophily theory. − − − − − − The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties". 第335行：
第243行： − + − − − − In the context of networks, social capital exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections. Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters. When two separate clusters possess non-redundant information, there is said to be a structural hole between them. Thus, a network that bridges structural holes will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes. − − − − − − Networks rich in structural holes are a form of social capital in that they offer information benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters. For example, in business networks, this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of weak ties, which rests on the basis that having a broad range of contacts is most effective for job attainment. 第358行：
第256行： − − − − − − − − − − − Communication Studies are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as sociology, psychology, anthropology, information science, biology, political science, and economics as well as rhetoric, literary studies, and semiotics. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network. − − − − − − − − In J.A. Barnes' day, a "community" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through telecommunications devices and social network services. Such devices and services require extensive and ongoing maintenance and analysis, often using network science methods. Community development studies, today, also make extensive use of such methods. − − − − − − − − Complex networks require methods specific to modelling and interpreting social complexity and complex adaptive systems, including techniques of dynamic network analysis. − − Mechanisms such as Dual-phase evolution explain how temporal changes in connectivity contribute to the formation of structure in social networks. − − − − − − − − In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength. − − − − − − − − Diffusion of ideas and innovations studies focus on the spread and use of ideas from one actor to another or one culture and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation. − − − − − − − − In demography, the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents. − − − − − − − − The field of sociology focuses almost entirely on networks of outcomes of social interactions. More narrowly, economic sociology considers behavioral interactions of individuals and groups through social capital and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy. − − − − − − − − Analysis of social networks is increasingly incorporated into health care analytics, not only in epidemiological studies but also in models of patient communication and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and systems. − − − − − − − Human ecology is an interdisciplinary and transdisciplinary study of the relationship between humans and their natural, social, and built environments. The scientific philosophy of human ecology has a diffuse history with connections to geography, sociology, psychology, anthropology, zoology, and natural ecology. − − − − − − − − Studies of language and linguistics, particularly evolutionary linguistics, focus on the development of linguistic forms and transfer of changes, sounds or words, from one language system to another through networks of social interaction. Social networks are also important in language shift, as groups of people add and/or abandon languages to their repertoire. − − − − − − − − In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo, De Nooy, and Senekal, to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of Even-Zohar, can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using visualization from SNA. − − − − − − Research studies of formal or informal organization relationships, organizational communication, economics, economic sociology, and other resource transfers. Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations. Intra-organizational networks have been found to affect organizational commitment, organizational identification, interpersonal citizenship behaviour. 第537行：
第337行： − − − − Social capital is a form of economic and cultural capital in which social networks are central, transactions are marked by reciprocity, trust, and cooperation, and market agents produce goods and services not mainly for themselves, but for a common good. Social capital is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners. The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. − − − − − Social capital is a sociological concept about the value of social relations and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use. In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity. − − − − − − <br /> − − <br /> − − Br / − − − − − − − − This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales. − − − − − + + − In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004"/> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref>{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement. − − In many organizations, members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities. Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement. − − 在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的玩家在发现和开发机会方面有优势。因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。 − − − − − − A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref>{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts. − − A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms. By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts. − 社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。+ + + + − − There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref>Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004"/> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career. − − There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations. However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted. Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career. − − 有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，'''罗纳德·伯特 Ronald Burt'''调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。因此，'''<font color="#32CD32">连接</font>'''结构洞对组织有益，反过来对个人的职业生涯也有益。 第619行：
第373行： + + + − {{main article|Social media}}+ − − − − [[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref>{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref>{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication. − − Computer networks combined with social networking software produces a new medium for social interaction. A relationship over a computerized social networking service can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a computer mediated communication context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of electronic commerce, information exchanged may also correspond to exchanges of money, goods or services in the "real" world. Social network analysis methods have become essential to examining these types of computer mediated communication. − − 计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在'''<font color="#ff8000">电脑中介传播 Computer-mediated Communication</font>'''环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着'''<font color="#ff8000">电子商务 Electronic Commerce</font>'''的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。 − − − − − − In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref>{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref> − − In addition, the sheer size and the volatile nature of social media has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data. − − 此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏鲁棒性。 − − − − =='''<font color="#32CD32">See also 参阅</font>'''== + − − − − − − − − − − − − − − − − − − − − − − − − − − − + − − }} − − }} − − }} − − 第726行：
第428行： − + − − − − − − − − − − − − − − − − − − − − − − − − + − + − − − − − − − − − {{Commons category|Social networks}} − − − − − − − − ===Organizations 组织=== − − − − + − − − − − − − − − − − − − − − − − − − − − + − − − − − − − − − − − − − − − − + − − − − − − − − − − − − − − − − − − − − − − − − 第901行：
第501行： − − − {{Social networking}} − − − − {{Social sciences}} − − − − {{Online social networking}} − − −
全文整理翻译编辑
{{Network Science}}
{{Network Science}}
A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.
A '''social network ''' is a [[social structure]] made up of a set of [[social]] actors (such as individuals or organizations), sets of [[Dyad (sociology)|dyadic]] ties, and other [[Social relation|social interactions]] between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures.<ref name="WF94CH1">{{cite book|last1=Wasserman |first1=Stanley | author-link1= Stanley Wasserman|last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |isbn=9780521387071 |chapter=Social Network Analysis in the Social and Behavioral Sciences |pages=1–27 |publisher=Cambridge University Press}}</ref> The study of these structures uses [[social network analysis]] to identify local and global patterns, locate influential entities, and examine network dynamics.
<font color="#ff8000">社会网络（Social Network）</font>是一种由一组社会行为者（个人或组织）、一组二元关系以及行为者之间的其他社会互动组成的社会结构。社会网络视角为分析整个社会实体的结构提供了一套方法，也为解释在这些结构中观察到的模式提供了各种理论。<ref name="WF94CH1" /> 通常使用<font color="#ff8000">社会网络分析（Social Network Analysis）对这些结构进行研究，</font>以确定本地及全球模式，定位有影响力的实体，并审查网络动态。
Social networks and the analysis of them is an inherently [[Interdisciplinarity|interdisciplinary]] academic field which emerged from [[social psychology]], [[sociology]], [[statistics]], and [[graph theory]]. [[Georg Simmel]] authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations".<ref name=":0">{{cite book|last1=Scott |first1=W. Richard |last2=Davis |first2=Gerald F. |author-link1= William Richard Scott|author-link2= Gerald F. Davis|title=Organizations and Organizing |chapter=Networks In and Around Organizations |year=2003 |isbn=978-0-13-195893-7 |publisher=Pearson Prentice Hall}}</ref> [[Jacob Moreno]] is credited with developing the first [[sociogram]]s in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the [[Social science|social and behavioral sciences]] by the 1980s.<ref name="WF94CH1" /><ref name="Freeman History">{{cite book|last=Freeman |first=Linton |year=2004 |publisher=Empirical Press |isbn=978-1-59457-714-7 |title=The Development of Social Network Analysis: A Study in the Sociology of Science}}</ref> [[Social network analysis]] is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other [[complex network]]s, it forms part of the nascent field of [[network science]].<ref name=":1">{{cite journal|journal=Science |year=2009 |volume=323 |number=5916 |pages=892–895 |doi=10.1126/science.1165821 |pmid=19213908 |title=Network Analysis in the Social Sciences |first1=Stephen P. |last1=Borgatti |first2=Ajay |last2=Mehra |first3=Daniel J. |last3=Brass |first4=Giuseppe |last4=Labianca|bibcode=2009Sci...323..892B |citeseerx=10.1.1.536.5568 }}</ref><ref name="EK">{{cite book|title=Networks, Crowds, and Markets: Reasoning about a Highly Connected World |first1=David |last1=Easley |first2=Jon |last2=Kleinberg |publisher=Cambridge University Press |year=2010 |chapter=Overview |pages=1–20 |isbn=978-0-521-19533-1}}</ref>
社会网络及其分析是社会心理学、社会学、统计学和图论中内在的跨学科学术领域。格奥尔格·齐美尔（Georg Simmel）是早期社会学结构主义理论的作者，他强调三人群体互动和“群体关系网”的动态性。<ref name=":0" /> 雅各布·莫雷诺（Jacob Moreno）被认为是在20世纪30年代发展了第一份<font color="#ff8000">社交关系图（Sociogram）</font>以研究人际关系的人。这些方法在20世纪50年代得到数学化，到20世纪80年代，社会网络的理论和方法在社会和行为科学中变得普遍。<ref name="WF94CH1" /><ref name="Freeman History" /> 目前，社会网络分析是当代社会学的主要范式之一，也被用于许多其他社会科学及形式科学。它与其他复杂网络一起构成了网络科学新兴领域的一部分。<ref name=":1" /><ref name="EK" />
==概览==
[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。|链接=Special:FilePath/Barabasi_Albert_model.gif]]
[[File:Barabasi Albert model.gif|thumb|left|图1：Evolution graph of a social network: [[Barabási–Albert model|Barabási model]].一个社会网络的演化图：巴拉巴西模型。]]
Barabási model.]]
Barabási model.]]
The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].
The social network is a [[Scientific theory|theoretical]] [[Construct (philosophy of science)|construct]] useful in the [[social sciences]] to study relationships between individuals, [[social groups|groups]], [[formal organizations|organizations]], or even entire [[society|societies]] ([[social unit]]s, see [[Differentiation (sociology)|differentiation]]). The term is used to describe a [[social structure]] determined by such [[social interactions|interactions]]. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An [[axiom]] of the social network approach to understanding [[social interaction]] is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that [[Agency (sociology)|individual agency]] is often ignored<ref name="jscott">Scott, John P. (2000). ''Social Network Analysis: A Handbook'' (2nd edition). Thousand Oaks, CA: Sage Publications.</ref> although this may not be the case in practice (see [[agent-based model]]ing). Precisely because many different types of relations, singular or in combination, form these network configurations, [[Network science|network analytics]] are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to [[anthropology]], [[biology]], [[communication studies]], [[economics]], [[geography]], [[information science]], [[organizational studies]], [[social psychology]], [[sociology]], and [[sociolinguistics]].
社会网络是社会科学中研究个人、团体、组织甚至整个社会（社会单位，见分化）之间关系的理论构造。这个术语用来描述由这种相互作用决定的社会结构。任何一个特定的社会单元之间的联系都代表着这个单元各种社会联系的聚合。这种理论方法必然是相关的。理解社会互动的社会网络方法的一个公理是，社会现象应该主要通过单元之间和单元内部关系的性质来构思和研究，而不是这些单元本身的性质。因此，社会网络理论总被诟病的一点是其常忽视个体代理<ref name="jscott" /> ，而实践中可能并非如此（见基于主体的建模）。正是因为许多不同类型的关系（单独或组合形式）形成这些网络配置，网络分析在广泛的研究中有用。在社会科学中，这些研究领域包括但不限于<font color="#ff8000">人类学</font> 、生物学、<font color="#ff8000">传播学</font>、经济学、地理学、信息科学、组织学、社会心理学、社会学和<font color="#ff8000">社会语言学</font>。
==History 历史==
==历史==
In the late 1890s, both [[Émile Durkheim]] and [[Ferdinand Tönnies]] foreshadowed the idea of social networks in their theories and research of [[social group]]s. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (''[[Gemeinschaft]]'', German, commonly translated as "[[community]]") or impersonal, formal, and instrumental social links (''[[Gesellschaft]]'', German, commonly translated as "[[society]]").<ref name=":2">Tönnies, Ferdinand (1887). ''Gemeinschaft und Gesellschaft'', Leipzig: Fues's Verlag. (Translated, 1957 by Charles Price Loomis as ''Community and Society'', East Lansing: Michigan State University Press.)</ref> Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors.<ref name=":3">Durkheim, Emile (1893). ''De la division du travail social: étude sur l'organisation des sociétés supérieures'', Paris: F. Alcan. (Translated, 1964, by Lewis A. Coser as ''The Division of Labor in Society'', New York: Free Press.)</ref> [[Georg Simmel]], writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.<ref name=":4">Simmel, Georg (1908). ''Soziologie'', Leipzig: Duncker & Humblot.</ref>
在19世纪90年代晚期，埃米尔·涂尔干 （Émile Durkheim）和斐迪南·滕尼斯（Ferdinand Tönnies）在他们关于社会群体的理论和研究中都预示了社会网络的概念。滕尼斯认为，社会群体可以作为个人和直接的社会关系存在，这种关系或者将具有共同价值观和信仰的个人（德语 Gemeinschaft，通常翻译为“社区”）联系在一起，或者将非个人的、正式的和工具性的社会关系（德语 Gesellschaft，通常翻译为“社会”）联系在一起。<ref name=":2" /> 涂尔干对社会事实给出了非个人主义的解释，认为当相互作用的个体构成一种再也不能用个体行为者的特性来解释的现实时，社会现象就产生了。<ref name=":3" />格奥尔格·齐美尔在20世纪之交的著作中指出了网络的本质和网络规模对互动的影响，并研究了在松散的网络而非群体中互动的可能性。<ref name=":4" />
[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]
[[File:Moreno Sociogram 2nd Grade.png|thumb|图2：Moreno's sociogram of a 2nd grade class 莫雷诺对一个二年级班级的社会关系图]]
Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name="jscott" /><ref>For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref>See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref>Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref>Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref>{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref>Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref>Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref>{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref>{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name="jscott" /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref>Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref>Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref>Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref>Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref>Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref>Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>
Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently.<ref name=jscott /><ref>For a historical overview of the development of social network analysis, see: {{cite book|last1=Carrington|first1=Peter J.|last2=Scott|first2=John|chapter=Introduction|title=The Sage Handbook of Social Network Analysis| publisher=Sage|year=2011|isbn=978-1-84787-395-8|page=1|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA1}}</ref><ref>See also the diagram in {{cite book|author=Scott, John|title=Social Network Analysis: A Handbook|publisher=Sage|year=2000|isbn=978-0-7619-6339-4|page=8|url=https://books.google.com/books?id=Ww3_bKcz6kgC&pg=PA8}}</ref> In [[psychology]], in the 1930s, [[Jacob L. Moreno]] began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see [[sociometry]]). In [[anthropology]], the foundation for social network theory is the theoretical and [[ethnography|ethnographic]] work of [[Bronislaw Malinowski]],<ref>Malinowski, Bronislaw (1913). ''The Family Among the Australian Aborigines: A Sociological Study''. London: University of London Press.</ref> [[Radcliffe-Brown|Alfred Radcliffe-Brown]],<ref>Radcliffe-Brown, Alfred Reginald (1930) ''The social organization of Australian tribes''. Sydney, Australia: University of Sydney ''Oceania'' monographs, No.1.</ref><ref>{{cite journal | last1 = Radcliffe-Brown | first1 = A. R. | year = 1940 | title = On social structure | url = | journal = Journal of the Royal Anthropological Institute | volume = 70 | issue = 1| pages = 1–12 | doi=10.2307/2844197| jstor = 2844197 }}</ref> and [[Claude Lévi-Strauss]].<ref>Lévi-Strauss, Claude ([1947]1967). ''Les structures élémentaires de la parenté''. Paris: La Haye, Mouton et Co. (Translated, 1969 by J. H. Bell, J. R. von Sturmer, and R. Needham, 1969, as ''The Elementary Structures of Kinship'', Boston: Beacon Press.)</ref> A group of social anthropologists associated with [[Max Gluckman]] and the [[Manchester school (anthropology)|Manchester School]], including [[John Arundel Barnes|John A. Barnes]],<ref>Barnes, John (1954). "Class and Committees in a Norwegian Island Parish". ''Human Relations'', (7): 39–58.</ref> [[J. Clyde Mitchell]] and [[Elizabeth Bott Spillius]],<ref>{{cite journal | last1 = Freeman | first1 = Linton C. | last2 = Wellman | first2 = Barry | year = 1995 | title = A note on the ancestoral Toronto home of social network analysis | url = | journal = Connections | volume = 18 | issue = 2| pages = 15–19 }}</ref><ref>{{cite journal | last1 = Savage | first1 = Mike | year = 2008 | title = Elizabeth Bott and the formation of modern British sociology | url = | journal = The Sociological Review | volume = 56 | issue = 4| pages = 579–605 | doi=10.1111/j.1467-954x.2008.00806.x}}</ref> often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom.<ref name=jscott /> Concomitantly, British anthropologist [[Siegfried Frederick Nadel|S. F. Nadel]] codified a theory of social structure that was influential in later network analysis.<ref>Nadel, S. F. 1957. ''The Theory of Social Structure''. London: Cohen and West.</ref> In [[sociology]], the early (1930s) work of [[Talcott Parsons]] set the stage for taking a relational approach to understanding social structure.<ref>Parsons, Talcott ([1937] 1949). ''The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers''. New York: The Free Press.</ref><ref>Parsons, Talcott (1951). ''The Social System''. New York: The Free Press.</ref> Later, drawing upon Parsons' theory, the work of sociologist [[Peter Blau]] provides a strong impetus for analyzing the relational ties of social units with his work on [[social exchange theory]].<ref>Blau, Peter (1956). ''Bureaucracy in Modern Society''. New York: Random House, Inc.</ref><ref>Blau, Peter (1960). "A Theory of Social Integration". ''The American Journal of Sociology'', (65)6: 545–556, (May).</ref><ref>Blau, Peter (1964). ''Exchange and Power in Social Life''.</ref>
20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见'''<font color="#ff8000">社会测量 Sociometry）</font>'''。在人类学中，社会网络理论的基础是'''布罗尼斯拉夫·马林诺夫斯基 Bronislaw Malinowski'''，'''阿尔弗雷德·拉德克利夫-布朗 Alfred Radcliffe-Brown'''和'''克洛德·列维-斯特劳斯 Claude Lévi-Strauss'''的理论和人种学著作。包括'''约翰·A·巴恩斯 John A. Barnes'''、'''J·克莱德·米切尔 J. Clyde Mitchell'''和'''伊丽莎白·博特·斯皮利厄斯 Elizabeth Bott Spillius'''在内的一群与'''马克斯·格拉克曼 Max Gluckman'''和'''曼彻斯特学派 Manchester School'''有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。在社会学方面，'''塔尔科特·帕森斯 Talcott Parsons'''的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。后来，社会学家'''彼得·布劳 Peter Blau'''的'''<font color="#ff8000">社会交换论 Social Exchange Theory</font>'''为分析社会单位之间的关系提供了强大的动力。
20世纪30年代，心理学、人类学和数学领域的几个独立研究小组已经看到了这一领域的重大发展。在心理学方面，在20世纪30年代，雅各布·L·莫雷诺开始系统地记录和分析小团体中的社会互动，尤其是课堂和工作团体中的社会互动（见'''<font color="#ff8000">社会测量 Sociometry）</font>'''。在人类学中，社会网络理论的基础是'''布罗尼斯拉夫·马林诺夫斯基 Bronislaw Malinowski'''，'''阿尔弗雷德·拉德克利夫-布朗 Alfred Radcliffe-Brown'''和'''克洛德·列维-斯特劳斯 Claude Lévi-Strauss'''的理论和人种学著作。包括'''约翰·A·巴恩斯 John A. Barnes'''、'''J·克莱德·米切尔 J. Clyde Mitchell'''和'''伊丽莎白·博特·斯皮利厄斯 Elizabeth Bott Spillius'''在内的一群与'''马克斯·格拉克曼 Max Gluckman'''和'''曼彻斯特学派 Manchester School'''有关的社会人类学家，经常被认为是执行了一些最初的实地工作，从而进行了网络分析，调查了南非、印度和英国的社区网络。在社会学方面，'''塔尔科特·帕森斯 Talcott Parsons'''的早期工作（1930年代）为采用关系方法理解社会结构奠定了基础。后来，社会学家'''彼得·布劳 Peter Blau'''的'''<font color="#ff8000">社会交换论 Social Exchange Theory</font>'''为分析社会单位之间的关系提供了强大的动力。
By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref>{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref>Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader"/><ref>Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref>Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref>Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>
By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist [[Harrison White]] and his students at the [[Harvard Department of Social Relations|Harvard University Department of Social Relations]]. Also independently active in the Harvard Social Relations department at the time were [[Charles Tilly]], who focused on networks in political and community sociology and social movements, and [[Stanley Milgram]], who developed the "six degrees of separation" thesis.<ref>{{cite web |url=http://www.semioticon.com/semiotix/semiotix14/sem-14-05.html |title=The Networked Individual: A Profile of Barry Wellman |author=Bernie Hogan}}</ref> [[Mark Granovetter]]<ref name="Introduction for the French Reader">{{cite journal | last1 = Granovetter | first1 = Mark | year = 2007 | title = Introduction for the French Reader | url = | journal = Sociologica | volume = 2 | issue = | pages = 1–8 }}</ref> and [[Barry Wellman]]<ref>Wellman, Barry (1988). "Structural analysis: From method and metaphor to theory and substance". pp. 19–61 in B. Wellman and S. D. Berkowitz (eds.) ''Social Structures: A Network Approach'', Cambridge, UK: Cambridge University Press.</ref> are among the former students of White who elaborated and championed the analysis of social networks.<ref name="Introduction for the French Reader"/><ref>Mullins, Nicholas. ''Theories and Theory Groups in Contemporary American Sociology''. New York: Harper and Row, 1973.</ref><ref>Tilly, Charles, ed. ''An Urban World''. Boston: Little Brown, 1974.</ref><ref>Wellman, Barry. 1988. "Structural Analysis: From Method and Metaphor to Theory and Substance". pp. 19–61 in ''Social Structures: A Network Approach'', edited by Barry Wellman and S. D. Berkowitz. Cambridge: Cambridge University Press.</ref>
到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家'''哈里森·怀特 Harrison White'''和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的'''查尔斯·堤利 Charles Tilly'''，还有发表了六度分隔理论论文的'''斯坦利·米尔格拉姆 Stanley Milgram'''。'''马克·格兰诺维特 Mark Granovetter'''和'''巴里·威尔曼 Barry Wellman'''是怀特以前的学生，他们阐述并支持对社交网络的分析。
到了20世纪70年代，越来越多的学者致力于将不同的轨迹和传统结合起来。其中一组由社会学家'''哈里森·怀特 Harrison White'''和他在哈佛大学社会关系系的学生组成。当时在哈佛大学社会关系系独立活动的还有专注于政治和社区社会学和社会运动的网络的'''查尔斯·堤利 Charles Tilly'''，还有发表了六度分隔理论论文的'''斯坦利·米尔格拉姆 Stanley Milgram'''。'''马克·格兰诺维特 Mark Granovetter'''和'''巴里·威尔曼 Barry Wellman'''是怀特以前的学生，他们阐述并支持对社交网络的分析。
Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.
Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as [[Duncan J. Watts]], [[Albert-László Barabási]], [[Peter Bearman]], [[Nicholas A. Christakis]], [[James H. Fowler]], and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.
从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如'''[[邓肯·瓦茨 Duncan J. Watts]]'''、'''[[艾伯特-拉斯洛·巴拉巴西 Albert-László Barabási]]'''、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。
从20世纪90年代末开始，社会网络分析经历了社会学家、政治学家和物理学家的工作，如'''[[邓肯·瓦茨 Duncan J. Watts]]'''、'''[[艾伯特-拉斯洛·巴拉巴西 Albert-László Barabási]]'''、Peter Bearman、Nicholas A. Christakis、James H. Fowler等人，开发和应用新的模型和方法来获得有关在线社会网络的新兴数据，以及有关面对面网络的“数字痕迹”。
[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>]]
[[File:Network self-organization stages.png|thumb|right|图3：Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 基于Nagler, Levina和Timme的自组织网络，(2011)<ref>{{cite journal|author1=Nagler, Jan|author2=Anna Levina|author3=Marc Timme|year=2011|title=Impact of single links in competitive percolation|journal=Nature Physics|volume=7|issue=3|pages=265–270|doi=10.1038/nphys1860|arxiv=1103.0922|bibcode=2011NatPh...7..265N}}</ref>|链接=Special:FilePath/Network_self-organization_stages.png]]
[[File:Social Network Diagram (large).svg|right|thumb|Centrality]]
[[File:Social Network Diagram (large).svg|right|thumb|Centrality|链接=Special:FilePath/Social_Network_Diagram_(large).svg]]
Centrality中心性
In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref>Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref>{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref>{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref>{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].
In general, social networks are [[self-organization|self-organizing]], [[emergence|emergent]], and [[social complexity|complex]], such that a globally coherent pattern appears from the local interaction of the elements that make up the system.<ref>Newman, Mark, Albert-László Barabási and Duncan J. Watts (2006). ''The Structure and Dynamics of Networks'' (Princeton Studies in Complexity). Oxford: Princeton University Press.</ref><ref>{{cite journal | last1 = Wellman | first1 = Barry | year = 2008 | title = Review: The development of social network analysis: A study in the sociology of science | url = | journal = Contemporary Sociology | volume = 37 | issue = 3| pages = 221–222 | doi=10.1177/009430610803700308}}</ref> These patterns become more apparent as network size increases. However, a global network analysis<ref>{{cite book|last=Faust|first=Stanley Wasserman; Katherine|title=Social network analysis : methods and applications|year=1998|publisher=Cambridge Univ. Press|location=Cambridge [u.a.]|isbn=978-0521382694|edition=Reprint.}}</ref> of, for example, all [[interpersonal relationships]] in the world is not feasible and is likely to contain so much [[Information theory|information]] as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.<ref name="Kadu12">Kadushin, C. (2012). ''Understanding social networks: Theories, concepts, and findings''. Oxford: Oxford University Press.</ref><ref>{{Cite journal| author=Granovetter, M.|title= Network sampling: Some first steps| year=1976 |pages=1287–1303 |volume=81| issue=6 |journal= American Journal of Sociology | doi=10.1086/226224}}</ref> The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although [[level of analysis|levels of analysis]] are not necessarily [[Mutually exclusive events|mutually exclusive]], there are three general levels into which networks may fall: [[Microsociology|micro-level]], [[wikt:meso-|meso-level]], and [[Macrosociology|macro-level]].
一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。
一般来说，社会网络是自组织的、涌现的和复杂的，这样，一个全局一致的模式就会从组成系统的元素的局部交互中显现出来。随着网络规模的增大，这些模式变得更加明显。然而，一个全球网络分析（如世界上所有的人际关系）是不可行的，它可能包含太多的信息，以至于相当于没有提供信息。计算能力的实际限制、道德规范以及参与者的招聘和报酬也限制了社会网络分析的规模。局部系统的细微差别在大规模网络分析中可能会消失，因此对于理解网络属性来说，信息的质量可能比其规模更重要。因此，社会网络是在与研究者的理论问题相关的尺度上加以分析的。虽然分析层次不一定相互排斥，但网络可以分为三个一般层次: 微观、中观和宏观。
At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.
At the micro-level, social network research typically begins with an individual, [[Snowball sampling|snowballing]] as social relationships are traced, or may begin with a small group of individuals in a particular social context.
在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。
在微观层面上，社会网络研究通常从个人开始，随着社会关系的追踪而像滚雪球一样扩大，或者可能从特定社会背景下的一小群个体开始。
'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].
'''Dyadic level''': A [[Dyad (sociology)|dyad]] is a social relationship between two individuals. Network research on dyads may concentrate on [[Structural functionalism|structure]] of the relationship (e.g. multiplexity, strength), [[social equality]], and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].
二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。
二元层面：二元是两个个体之间的社会关系。网络对二元关系的研究可以集中在关系的结构上（如多样性、力量)、社会平等以及互惠互利的倾向。
'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.
'''Triadic level''': Add one individual to a dyad, and you have a [[Triadic relation|triad]]. Research at this level may concentrate on factors such as [[Independence number|balance]] and [[Vertex-transitive graph|transitivity]], as well as [[social equality]] and tendencies toward [[Reciprocity (social and political philosophy)|reciprocity/mutuality]].<ref name="Kadu12"/> In the [[balance theory]] of [[Fritz Heider]] the triad is the key to social dynamics. The discord in a rivalrous [[love triangle]] is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of [[signed graph]]s.
三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。在'''弗里茨 · 海德 Fritz Heider'''的'''平衡理论 Balance Theory'''中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。
三元层面：再加一个个体到二元组中，就得到一个三元组。这一层次的研究可能集中在诸如平衡和传递性等因素，以及社会平等和互惠互利的倾向。在'''弗里茨 · 海德 Fritz Heider'''的'''平衡理论 Balance Theory'''中，三元组是社会动力学的关键。在敌对的三角恋中的不和谐是不平衡的三角关系的一个例子，很可能通过其中一种关系的改变而变成平衡的三角关系。社会中社会友谊的动态模型是通过平衡三角关系建立起来的。该研究利用了符号图理论。
'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.
'''Actor level''': The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, [[centrality]], [[wikt:prestige|prestige]] and roles such as [[isolates|isolates, liaisons]], and [[Bridge (interpersonal)|bridges]].<ref name="Jone11"/> Such analyses, are most commonly used in the fields of [[psychology]] or [[Social psychology (sociology)|social psychology]], [[ethnographic]] [[kinship]] analysis or other [[genealogy|genealogical]] studies of relationships between individuals.
行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。
行为者层面：社会网络中最小的分析单位是社会环境中的个体，即“行为者”或“自我”。自我网络分析主要关注网络特征，例如大小、关系强度、密度、中心性、声望和隔离、联络和桥梁等角色。这种分析最常用于心理学或社会心理学、人种学亲属关系分析或其他个体关系的系谱研究领域。
'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref>{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>
'''Subset level''': [[Subset]] levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on [[Distance (graph theory)|distance]] and reachability, [[cliques]], [[Cohesion (social policy)|cohesive]] subgroups, or other [[Group action (sociology)|group actions]] or [[Group behaviour|behavior]].<ref>{{cite book | title="Graph Theoretical Approaches to Social Network Analysis". in Computational Complexity: Theory, Techniques, and Applications (Robert A. Meyers, ed.) | publisher=Springer | author=de Nooy, Wouter |year=2012 | pages=2864–2877 | isbn=978-1-4614-1800-9|doi=10.1007/978-1-4614-1800-9_176| chapter=Social Network Analysis, Graph Theoretical Approaches to }}</ref>
子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。
子集层面：网络研究问题的子集级别开始于微观级别，但可能跨越到中观级别的分析。子集级别的研究可能集中在距离和可达性、派系、凝聚子群或其他群体行为或行为。
In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref>{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>
In general, meso-level theories begin with a [[Sample population|population]] size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.<ref>{{cite journal | last1 = Hedström | first1 = Peter | last2 = Sandell | first2 = Rickard | last3 = Stern | first3 = Charlotta | year = 2000 | title = Mesolevel Networks and the Diffusion of Social Movements: The Case of the Swedish Social Democratic Party | url = http://www.nuffield.ox.ac.uk/users/hedstrom/ajs3.pdf | journal = American Journal of Sociology | volume = 106 | issue = 1| pages = 145–172 | doi = 10.1086/303109 }}</ref>
一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。
一般来说，中观层面的理论始于介于微观和宏观层面之间的规模。然而，中观层面也可以指专门为揭示微观和宏观层面之间的联系而设计的分析。中观层次的网络是密度低，可能表现出不同于人际微观层面网络的因果过程。
[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图]]
[[File:Social Red.jpg|thumb|right|图4：Social network diagram, meso-level 中观层面社会网络图|链接=Special:FilePath/Social_Red.jpg]]
'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref>{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>
'''Organizations''': Formal [[organizations]] are [[social group]]s that distribute tasks for a collective [[goal]].<ref name="Riketta, M. 2007">{{cite journal | last1 = Riketta | first1 = M. | last2 = Nienber | first2 = S. | year = 2007 | title = Multiple identities and work motivation: The role of perceived compatibility between nested organizational units | url = | journal = British Journal of Management | volume = 18 | issue = | pages = S61–77 | doi=10.1111/j.1467-8551.2007.00526.x}}</ref> Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of [[Formal organization|formal]] or [[Informal organization|informal]] relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures.<ref name="Riketta, M. 2007"/> Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.<ref>{{Cite journal|last=Shirado|first=Hirokazu|last2=Christakis|first2=Nicholas A|title=Locally noisy autonomous agents improve global human coordination in network experiments|journal=Nature|volume=545|issue=7654|pages=370–374|doi=10.1038/nature22332|pmid=28516927|pmc=5912653|bibcode=2017Natur.545..370S|year=2017}}</ref>
组织: 正规组织是为共同目标分配任务的社会团体。关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。
组织: 正规组织是为共同目标分配任务的社会团体。关于组织的网络研究可以侧重于正式或非正式关系方面的组织内或组织间联系。组织内网络本身往往包含多层次的分析，特别是在具有多个分支机构、特许权或半自治部门的较大组织中。在这些情况下，研究通常在工作组和组织层面进行，重点放在两个结构之间的相互作用。
'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref>{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>
'''Randomly distributed networks''': [[Exponential random graph models]] of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general [[Degree (graph theory)|degree]]-based structural effects commonly observed in many human social networks as well as [[Reciprocity (social and political philosophy)|reciprocity]] and [[Transitive set|transitivity]], and at the node-level, [[homophily]] and [[Attribute-value system|attribute]]-based activity and popularity effects, as derived from explicit hypotheses about [[Dependency graph|dependencies]] among network ties. [[Parameter]]s are given in terms of the prevalence of small [[Induced subgraph|subgraph]] configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.<ref>{{cite journal | last1 = Cranmer | first1 = Skyler J. | last2 = Desmarais | first2 = Bruce A. | year = 2011 | title = Inferential Network Analysis with Exponential Random Graph Models | url = | journal = Political Analysis | volume = 19 | issue = 1| pages = 66–86 | doi=10.1093/pan/mpq037| citeseerx = 10.1.1.623.751 }}</ref>
随机分布的网络: 社会网络的'''<font color="#ff8000">指数随机图模型 Exponential random graph models</font>'''在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。
随机分布的网络: 社会网络的'''<font color="#ff8000">指数随机图模型 Exponential random graph models</font>'''在20世纪80年代成为最先进的社会网络分析方法。这个框架有能力表示在许多人类社会网络中普遍观察到的社会结构效应，包括在许多人类社会网络中普遍观察到的基于程度的一般性结构效应以及互惠性和传递性，以及在节点一级、同相性和基于属性的活动和流行性效应，这些效应源于关于网络关系之间依赖性的明确假设。参数是根据网络中小型子图配置的流行程度给出的，可以解释为描述一个给定网络出现的局部社会过程的组合。这些网络的概率模型在给定的参与者集合上允许超越微型网络的限制性并元独立性假设的泛化，允许模型从社会行为的理论结构基础上建立。
'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref>{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref>Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.
'''Scale-free networks''': A [[scale-free network]] is a [[complex network|network]] whose [[degree distribution]] follows a [[power law]], at least [[asymptotic]]ally. In [[network theory]] a scale-free ideal network is a [[random network]] with a [[degree distribution]] that unravels the size distribution of social groups.<ref>{{cite journal |author1=Moreira, André A.|author2=Demétrius R. Paula|author3=Raimundo N. Costa Filho|author4=José S. Andrade, Jr. |title=Competitive cluster growth in complex networks |year=2006 |doi=10.1103/PhysRevE.73.065101 |pmid=16906890 |journal=Physical Review E |volume=73 |issue=6 |pages=065101 |arxiv=cond-mat/0603272 |bibcode=2006PhRvE..73f5101M }}</ref> Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of [[Vertex (graph theory)|vertices]] with a [[Maximum degree|degree]] that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the [[clustering coefficient]] distribution, which decreases as the node degree increases. This distribution also follows a [[power law]].<ref>Barabási, Albert-László (2003). ''Linked: how everything is connected to everything else and what it means for business, science, and everyday life''. New York: Plum.</ref> The [[Barabási–Albert model|Barabási]] model of network evolution shown above is an example of a scale-free network.
'''<font color="#ff8000">[[无标度网络]] A Scale-free Network</font>''': 无标度网络网络是一个'''<font color="#ff8000">度分布 Degree Distribution</font>'''遵循'''<font color="#ff8000">幂律 Power Law</font>'''(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“'''<font color="#ff8000">枢纽 Hubs</font>'''” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是'''<font color="#ff8000">集聚系数 General Characteristic</font>'''分布，它随着节点度的增加而减少。该分布也遵循幂律。上面网络演化的巴拉巴西模型就是无标度网络的一个例子。
'''<font color="#ff8000">[[无标度网络]] A Scale-free Network</font>''': 无标度网络网络是一个'''<font color="#ff8000">度分布 Degree Distribution</font>'''遵循'''<font color="#ff8000">幂律 Power Law</font>'''(至少是渐近的）的网络。在网络理论中，无标度理想网络是具有揭示了社会群体的规模分布的度分布的随机网络。无标度网络的具体特征随创建其的理论和分析工具的变化而变化，然而，一般来说，无标度网络具有一些共同的特征。无标度网络的一个显著特征是度远超均值的顶点的相对共性。最高度的节点通常被称为“'''<font color="#ff8000">枢纽 Hubs</font>'''” ，并且可能在其网络中服务于特定的目的，尽管这在很大程度上取决于社会环境。无标度网络的另一个一般特性是'''<font color="#ff8000">集聚系数 General Characteristic</font>'''分布，它随着节点度的增加而减少。该分布也遵循幂律。上面网络演化的巴拉巴西模型就是无标度网络的一个例子。
Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].
Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as [[economic]] or other [[resource]] [[Transfer function|transfer]] interactions over a large [[Sample population|population]].
宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。
宏观层面的分析不是追踪人际互动，而通常是追踪相互作用的结果，例如经济或其他资源转移在一大群体中的相互作用。
[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部]]
[[File:Diagram of a social network.jpg|thumb|right|图6: Diagram: section of a large-scale social network 一大型社会网络局部|链接=Special:FilePath/Diagram_of_a_social_network.jpg]]
'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).
'''Large-scale networks''': [[Large-scale macroeconometric model|Large-scale network]] is a term somewhat synonymous with "macro-level" as used, primarily, in [[social science|social]] and [[Behavioural sciences|behavioral]] sciences, in [[economics]]. Originally, the term was used extensively in the [[computer sciences]] (see [[Network mapping#Large-scale mapping project|large-scale network mapping]]).
大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。
大规模网络: 大规模网络是一个与“宏观层面”同义的术语，主要用于社会和行为科学，经济学。最初，这个术语在计算机科学中广泛使用（见大规模网络映射）。
'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref>{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>
'''Complex networks''': Most larger social networks display features of [[social complexity]], which involves substantial non-trivial features of [[network topology]], with patterns of complex connections between elements that are neither purely regular nor purely random (see, [[complexity science]], [[dynamical system]] and [[chaos theory]]), as do [[biological]], and [[Computer network|technological networks]]. Such [[complex network]] features include a heavy tail in the [[degree distribution]], a high [[clustering coefficient]], [[assortativity]] or disassortativity among vertices, [[community structure]] (see [[stochastic block model]]), and [[hierarchy|hierarchical structure]]. In the case of [[Agency (philosophy)|agency-directed]] networks these features also include [[Reciprocity in network|reciprocity]], triad significance profile (TSP, see [[network motif]]), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as [[lattice graph|lattices]] and [[random graph]]s, do not show these features.<ref>{{cite journal|author=Strogatz, Steven H.|year=2001|title=Exploring complex networks|journal=Nature|volume=410|pages=268–276|doi=10.1038/35065725|pmid=11258382|issue=6825|bibcode=2001Natur.410..268S|doi-access=free}}</ref>
复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括'''<font color="#ff8000">网络拓扑 Network Topology</font>'''的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见[[复杂性科学]]、[[动力系统]]和[[混沌理论]]），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的'''<font color="#ff8000">同配性 Assortativity</font>'''或非同配性、社区结构（见'''<font color="#ff8000">随机分块模型 Stochastic Block Model</font>'''）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、'''<font color="#32CD32">三重显著性特征</font>'''（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和'''<font color="#ff8000">随机图 Random Graph</font>'''，并没有表现出这些特征。
复杂网络: 大多数较大的社会网络呈现出社会复杂性的特征，包括'''<font color="#ff8000">网络拓扑 Network Topology</font>'''的大量非平凡特征，以及既不完全规则也不完全随机的元素之间的复杂连接模式（见[[复杂性科学]]、[[动力系统]]和[[混沌理论]]），生物和技术网络也是如此。这些复杂的网络特征包括度分布的重尾、高集聚系数、顶点之间的'''<font color="#ff8000">同配性 Assortativity</font>'''或非同配性、社区结构（见'''<font color="#ff8000">随机分块模型 Stochastic Block Model</font>'''）和层次结构。在主体导向网络的情况下，这些特征还包括互惠性、'''<font color="#32CD32">三重显著性特征</font>'''（TSP，见网络基序）及其他。相比之下，许多过去研究过的网络数学模型，如格和'''<font color="#ff8000">随机图 Random Graph</font>'''，并没有表现出这些特征。
==Theoretical links 理论联系==
==Theoretical links 理论联系==
=== Imported theories 输入的理论 ===
===Imported theories 输入的理论===
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref>{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>
Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are [[Graph theory]], [[Balance theory]], Social comparison theory, and more recently, the [[Social identity approach]].<ref>{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>
为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是[[图论]]、'''<font color="#ff8000">平衡理论 Balance theory</font>'''、社会比较论，以及最近的社会认同方法。
为了使用社会网络分析，已经引入了各种理论框架。其中最突出的是[[图论]]、'''<font color="#ff8000">平衡理论 Balance theory</font>'''、社会比较论，以及最近的社会认同方法。
===Indigenous theories 本土理论===
===Indigenous theories 本土理论===
Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].
Few complete theories have been produced from social network analysis. Two that have are [[role theory|structural role theory]] and [[heterophily|heterophily theory]].
很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。
很少有完整的理论产生于社会网络分析。现有两个为结构角色理论和异质性理论。
The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref>{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>
The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".<ref>{{Cite journal | author=Granovetter, M. |year=1973 |title= The strength of weak ties |journal= American Journal of Sociology |volume=78 |issue=6 |pages= 1360–1380 |doi=10.1086/225469}}</ref>
异质性理论的基础是在一项研究中发现，更多的'''<font color="#ff8000">弱关系 Weak Tie</font>'''可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。
异质性理论的基础是在一项研究中发现，更多的'''<font color="#ff8000">弱关系 Weak Tie</font>'''可以在寻求信息和创新方面发挥重要作用，因为小圈子倾向于有更同质化的观点，也有许多共同特征。这种亲同性倾向是小圈子成员被吸引到一起的首要原因。然而，由于相似，小圈子里的每一个成员或多或少都知道其他成员所知道的事情。为了获得新的信息或见解，小圈子里的成员不得不超越该圈子，关注其他的朋友及熟人。这就是格兰诺维特所说的“弱关系的力量”。
==Structural holes 结构洞==
==Structural holes 结构洞==
In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992" /> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992" />
In the context of networks, [[social capital]] exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections.<ref name="Burt 2004">{{cite journal|last=Burt|first=Ronald|title=Structural Holes and Good Ideas|journal=American Journal of Sociology|year=2004|doi=10.1086/421787|volume=110|issue=2|pages=349–399|citeseerx=10.1.1.388.2251}}</ref> Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters.<ref name="Burt 1992">{{cite book|last=Burt|first=Ronald|title=Structural Holes: The Social Structure of Competition|year=1992|publisher=Harvard University Press|location=Cambridge, MA}}</ref> When two separate clusters possess non-redundant information, there is said to be a structural hole between them.<ref name="Burt 1992"/> Thus, a network that bridges [[structural holes]] will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.<ref name="Burt 1992"/>
在网络的背景下，'''<font color="#ff8000">社会资本 Social Capital</font>'''存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个'''<font color="#ff8000">结构洞 Structural Hole</font>'''。因此，一个'''<font color="#32CD32">连接</font>'''结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。
在网络的背景下，'''<font color="#ff8000">社会资本 Social Capital</font>'''存在于人们因其在网络中的位置而具有优势的地方。网络中的联系提供的信息、机会和观点可能有利于网络中的核心参与者。大多数社会结构往往以有强连接的密集集群为特征。这些集群中的信息往往是相当同质和冗余的。非冗余信息通常是通过不同集群中的联系获得的。当两个独立的集群拥有非冗余信息时，我们称它们之间存在一个'''<font color="#ff8000">结构洞 Structural Hole</font>'''。因此，一个'''<font color="#32CD32">连接</font>'''结构孔的网络在某种程度上提供附加而非重叠的的网络效益。理想的网络结构具有蔓生结构和集群结构，可访问许多不同集群和结构洞。
Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.
Networks rich in structural holes are a form of social capital in that they offer [[Information theory|information]] benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters.<ref name="Burt 1992"/> For example, in [[Business networking|business networks]], this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of [[Interpersonal ties|weak ties]], which rests on the basis that having a broad range of contacts is most effective for job attainment.
富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。例如，在'''<font color="#ff8000">商业社交 Business networking</font>'''中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。
富含结构洞的网络是社会资本的一种形式，因为它们提供信息利益。连接结构洞的网络中的主要参与者能够访问来自不同来源和集群的信息。例如，在'''<font color="#ff8000">商业社交 Business networking</font>'''中，这对个人的职业生涯是有益的，因为若其关系网涵盖不同行业 / 部门，则更有可能得知职位空缺和机会。这个概念类似于马克·格兰诺维特的弱关系理论，该理论的基础是拥有广泛的联系对于获得工作是最有效的。
==Research clusters 研究集群==
==Research clusters 研究集群==
===Communication 传播学===
===Communication 传播学===
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.
[[Communication Studies]] are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as [[sociology]], [[psychology]], [[anthropology]], [[information science]], [[biology]], [[political science]], and [[economics]] as well as [[rhetoric]], [[literary studies]], and [[semiotics]]. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network.
传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。
传播学通常被认为是社会科学和人文科学的一部分，主要集中在社会学、心理学、人类学、信息科学、生物学、政治学、经济学以及修辞学、文学研究和符号学等领域。许多通信概念描述了从一个源到另一个源的信息传输，因此可以从网络的角度来考虑。
===Community 社区===
===Community 社区===
In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.
In J.A. Barnes' day, a "[[community]]" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through [[telecommunications]] devices and [[social network services]]. Such devices and services require extensive and ongoing maintenance and analysis, often using [[network science]] methods. [[Community development]] studies, today, also make extensive use of such methods.
在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。
在J.A. Barnes' day，“社区”指的是一个特定的地理位置和与谁交谈、联系、贸易和一同上教堂做礼拜有关的社区关系的研究。然而如今，通过电信设备和社交网络服务，有了扩展的“在线”社区。这样的设备和服务需要广泛和持续的维护和分析，通常使用网络科学方法。社区发展研究在如今也广泛使用这些方法。
===Complex networks 复杂网络===
===Complex networks 复杂网络===
[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].
[[Complex networks]] require methods specific to modelling and interpreting [[social complexity]] and [[complex adaptive system]]s, including techniques of [[dynamic network analysis]].
复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。
复杂网络需要建模和解释社会复杂性和复杂适应系统的特定方法，包括动态网络分析技术。
Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.
Mechanisms such as [[Dual-phase evolution#social networks|Dual-phase evolution]] explain how temporal changes in connectivity contribute to the formation of structure in social networks.
'''<font color="#ff8000">双相演化理论 Dual-phase Evolution</font>'''等机制解释了连接性的时间变化如何促进社会网络结构的形成。
'''<font color="#ff8000">双相演化理论 Dual-phase Evolution</font>'''等机制解释了连接性的时间变化如何促进社会网络结构的形成。
===Criminal networks 犯罪网络===
===Criminal networks 犯罪网络===
In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.
In [[criminology]] and [[urban sociology]], much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos<ref>{{cite journal |last=Papachristos |first=Andrew |year=2009 |title=Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide |journal=American Journal of Sociology |volume=115 |issue=1 |pages=74–128 |doi=10.2139/ssrn.855304 |url=http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |accessdate=29 March 2013 |url-status=dead |archiveurl=https://web.archive.org/web/20140407094725/http://www.papachristos.org/Publications_2_files/ajs_final_version.pdf |archivedate=7 April 2014 }}</ref> has studied gang murders as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.
在'''<font color="#ff8000">犯罪学 Criminology</font>和'''<font color="#ff8000">城市社会学 Urban sociology</font>中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。
在'''<font color="#ff8000">犯罪学 Criminology</font>和'''<font color="#ff8000">城市社会学 Urban sociology</font>中，犯罪行为人之间的社会网络问题受到了广泛的关注。例如，'''安德鲁·帕帕克里斯托斯 Andrew Papachristos'''将帮派谋杀研究为帮派之间的一系列交流。谋杀可以视为从单一来源向外扩散，因为较弱的帮派无法承担为报复杀死较强帮派成员的代价，而必须采取其他暴力行动来维护其势力强大之名声。
===Diffusion of innovations 创新扩散理论===
===Diffusion of innovations 创新扩散理论===
[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.
[[Diffusion of innovations|Diffusion of ideas and innovations]] studies focus on the spread and use of ideas from one actor to another or one [[culture]] and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation.
'''<font color="#ff8000">创新扩散理论 Diffusion of Innovations</font>'''研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。
'''<font color="#ff8000">创新扩散理论 Diffusion of Innovations</font>'''研究的重点是思想从一个行动者到另一个或一种文化到另一种的传播。这一系列的研究试图解释为什么有些人成为创意和创新的“早期接受者” ，并将社交网络结构与促进或阻碍创新的传播联系起来。
===Demography 人口学===
===Demography 人口学===
In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.
In [[demography]], the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.
在'''<font color="#ff8000">人口学 Demography</font>'''方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。
在'''<font color="#ff8000">人口学 Demography</font>'''方面，对社会网络的研究导致了新的抽样方法，用于估计和覆盖难以统计的人群（如无家可归者或静脉注射毒品者）。例如，受访者驱动的抽样依赖于调查的受访者推荐更多的受访者，是一种基于网络的抽样技术。
===Economic sociology 经济社会学===
===Economic sociology 经济社会学===
The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref>{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>
The field of [[sociology]] focuses almost entirely on networks of outcomes of social interactions. More narrowly, [[economic sociology]] considers behavioral interactions of individuals and groups through [[social capital]] and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.<ref>{{cite journal |last=Granovetter |first=Mark |year=2005 |title=The Impact of Social Structure on Economic Outcomes |journal=The Journal of Economic Perspectives |jstor=4134991 |volume=19 |issue=1 |pages=33–50|doi=10.1257/0895330053147958 }}</ref>
社会学领域几乎完全关注社会互动的结果网络。更狭义地说，'''<font color="#ff8000">经济社会学 Economic Sociology</font>'''通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。
社会学领域几乎完全关注社会互动的结果网络。更狭义地说，'''<font color="#ff8000">经济社会学 Economic Sociology</font>'''通过社会资本和社会“市场”考虑个人和群体的行为互动。社会学家，如马克·格兰诺维特，已经研究出关于社会结构、信息、奖惩能力和信任相互作用的核心原则，这些原则在他们对政治、经济和其他制度的分析中经常出现。格兰诺维特研究了社会结构和社会网络如何影响经济结果，如雇佣、价格、生产力和创新，并描述了社会学家对分析社会结构和网络对经济的影响的贡献。
===Health care 卫生保健===
===Health care 卫生保健===
Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>
Analysis of social networks is increasingly incorporated into [[health care analytics]], not only in [[epidemiology|epidemiological]] studies but also in models of [[Health Communication|patient communication]] and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and [[health care systems|systems]].<ref>Levy, Judith and Bernice Pescosolido (2002). ''Social Networks and Health''. Boston, MA: JAI Press.</ref>
社会网络分析越来越多地被纳入卫生保健分析——不仅在'''<font color="#ff8000">流行病学 Epidemiology</font>'''研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。
社会网络分析越来越多地被纳入卫生保健分析——不仅在'''<font color="#ff8000">流行病学 Epidemiology</font>'''研究中，而且在病人沟通和教育、疾病预防、心理健康诊断和治疗模型中，以及在卫生保健组织和系统的研究中。
===Human ecology 人类生态学===
===Human ecology 人类生态学===
[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref>Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref>Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>
[[Human ecology]] is an [[interdisciplinary]] and [[transdisciplinary]] study of the relationship between [[human]]s and their [[natural environment|natural]], [[Social environment|social]], and [[built environment]]s. The scientific philosophy of human ecology has a diffuse history with connections to [[geography]], [[sociology]], [[psychology]], [[anthropology]], [[zoology]], and natural [[ecology]].<ref>Crona, Beatrice and Klaus Hubacek (eds.) (2010). [http://www.ecologyandsociety.org/issues/view.php?sf=48 "Special Issue: Social network analysis in natural resource governance"]. ''Ecology and Society'', 48.</ref><ref>Ernstson, Henrich (2010). "Reading list: Using social network analysis (SNA) in social-ecological studies". [http://rs.resalliance.org/2010/11/03/reading-list-using-social-network-analysis-sna-in-social-ecological-studies/ ''Resilience Science'']</ref>
'''<font color="#ff8000">人类生态学 Human ecology</font>'''是研究人类与其自然环境、社会环境和'''<font color="#ff8000">建成环境 Built environment</font>'''之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。
'''<font color="#ff8000">人类生态学 Human ecology</font>'''是研究人类与其自然环境、社会环境和'''<font color="#ff8000">建成环境 Built environment</font>'''之间关系的一门跨学科科学。人类生态学的科学哲学与地理学、社会学、心理学、人类学、动物学和自然生态学有着密切的联系。
===Language and linguistics 语言与语言学===
===Language and linguistics 语言与语言学===
Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.
Studies of [[language]] and [[linguistics]], particularly [[evolutionary linguistics]], focus on the development of [[Morphology (linguistics)|linguistic forms]] and transfer of changes, [[Phonology|sounds]] or words, from one language system to another through networks of social interaction. Social networks are also important in [[language shift]], as groups of people add and/or abandon languages to their repertoire.
语言学和语言学的研究，特别是'''<font color="#ff8000">演化语言学 Evolutionary Linguistics</font>'''，关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。
语言学和语言学的研究，特别是'''<font color="#ff8000">演化语言学 Evolutionary Linguistics</font>'''，关注通过社会互动网络从一个语言系统转移到另一个语言系统时，语言形式的发展以及声音或词语的变化。社交网络在语言转换中也很重要，因为一些人群增加或者放弃了他们的语言。
===Literary networks 文学网络===
===Literary networks 文学网络===
In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref>{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref>{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ʼn Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.
In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H. K. | last2 = Romo | first2 = F. P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = | journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> De Nooy,<ref>{{cite journal | last1 = De Nooy | first1 = W | year = 2003| title = Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory | url = | journal = Poetics | volume = 31 | issue = 5–6| pages = 305–327 | doi = 10.1016/S0304-422X(03)00035-4 }}</ref> and Senekal,<ref>{{cite journal | last1 = Senekal | first1 = B. A. | year = 2012 | title = Die Afrikaanse literêre sisteem: ʼn Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA) | url = | journal = LitNet Akademies | volume = 9 | issue = | page = 3 }}</ref> to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of [[Even-Zohar]], can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using [[Computer graphics (computer science)|visualization]] from SNA.
在文学体系的研究中，网络分析被'''阿海尔 Anheier'''、'''格尔哈兹 Gerhards'''和'''罗姆 Romo'''、'''努伊 De Nooy'''和Senekal应用于研究文学如何运作的各个方面。其基本前提是将'''文-佐哈尔 Even-Zohar'''著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。
在文学体系的研究中，网络分析被'''阿海尔 Anheier'''、'''格尔哈兹 Gerhards'''和'''罗姆 Romo'''、'''努伊 De Nooy'''和Senekal应用于研究文学如何运作的各个方面。其基本前提是将'''文-佐哈尔 Even-Zohar'''著述以来就存在的多元系统理论可以与网络理论相结合，以及文学网络中不同行为者（如作家、评论家、出版商、文学史等）之间的关系可以通过SNA可视化映射。
===Organizational studies 组织研究===
===Organizational studies 组织研究===
Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref>{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref>{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref>{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>
Research studies of [[Formal organization|formal]] or [[informal organization]] [[Social relation|relationships]], [[organizational communication]], [[economics]], [[economic sociology]], and other [[resource]] [[Transfer function|transfers]]. Social networks have also been used to examine how organizations interact with each other, characterizing the many [[Interlocking directorate|informal connections]] that link executives together, as well as associations and connections between individual employees at different organizations.<ref>{{cite journal | last1 = Podolny | first1 = J. M. | last2 = Baron | first2 = J. N. | year = 1997 | title = Resources and relationships: Social networks and mobility in the workplace | url = | journal = American Sociological Review | volume = 62 | issue = 5| pages = 673–693 | doi=10.2307/2657354| jstor = 2657354 | citeseerx = 10.1.1.114.6822 }}</ref> Intra-organizational networks have been found to affect [[organizational commitment]],<ref>{{cite journal | last1 = Lee | first1 = J. | last2 = Kim | first2 = S. | year = 2011 | title = Exploring the role of social networks in affective organizational commitment: Network centrality, strength of ties, and structural holes | url = | journal = The American Review of Public Administration | volume = 41 | issue = 2| pages = 205–223 | doi=10.1177/0275074010373803}}</ref> [[organizational identification]],<ref name="Jone11">{{cite journal | last1 = Jones | first1 = C. | last2 = Volpe | first2 = E.H. | year = 2011 | title = Organizational identification: Extending our understanding of social identities through social networks | url = | journal = Journal of Organizational Behavior | volume = 32 | issue = 3| pages = 413–434 | doi=10.1002/job.694}}</ref> [[Organizational citizenship behavior|interpersonal citizenship behaviour]].<ref>{{cite journal | last1 = Bowler | first1 = W. M. | last2 = Brass | first2 = D. J. | year = 2011 | title = Relational correlates of interpersonal citizenship behaviour: A social network perspective | doi = 10.1037/0021-9010.91.1.70 | pmid = 16435939 | journal = Journal of Applied Psychology | volume = 91 | issue = 1| pages = 70–82 | citeseerx = 10.1.1.516.8746 }}</ref>
研究正式或非正式组织关系、'''<font color="#ff8000">组织沟通 Organizational Communication</font>'''、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。研究发现，组织内网络对'''<font color="#ff8000">组织承诺 Organizational Commitment</font>'''、组织认同、人际公民行为有影响。
研究正式或非正式组织关系、'''<font color="#ff8000">组织沟通 Organizational Communication</font>'''、经济、经济社会学和其他资源转移。社交网络也被用来研究组织之间如何相互作用，描述了许多将高管联系在一起的非正式联系，以及不同组织的个体雇员之间的联系。研究发现，组织内网络对'''<font color="#ff8000">组织承诺 Organizational Commitment</font>'''、组织认同、人际公民行为有影响。
===Social capital 社会资本===
===Social capital 社会资本===
[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref>(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref>(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref>(Claridge, 2018).</ref>
[[Social capital]] is a form of [[Capital (economics)|economic]] and [[cultural capital]] in which social networks are central, [[Stock and flow|transactions]] are marked by [[Reciprocity (social psychology)|reciprocity]], [[Trust (social sciences)|trust]], and [[cooperation]], and [[Market (economics)|market]] [[Agent (economics)|agents]] produce [[goods and services]] not mainly for themselves, but for a [[common good]]. [[Social capital]] is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations.<ref>(Claridge, 2018).</ref>. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and idenfications of the bonds between partners.The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. <ref>(Claridge, 2018).</ref> The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions. <ref>(Claridge, 2018).</ref>
社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。 关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。
社会资本是一种以社会网络为中心的经济和文化资本，交易以互惠、信任和合作为特征，市场主体生产的商品和服务主要不是为了自已，而是为了共同的利益。社会资本分为三个维度: 结构维度、关系维度和认知维度。结构维度描述了合作伙伴之间如何相互作用，以及哪些特定的合作伙伴在社交网络中相遇。社会资本的结构维度反映了组织之间的关系水平。这个维度与关系维度高度相关，关系维度指的是伙伴之间关系的可信度、规范、期望和认同度。 关系维度解释了这些联系的本质，主要表现在对组织网络的信任程度上。认知维度分析组织在多大程度上因其联系和相互作用而共享共同的目标和目的。
[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref>{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref>{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>
[[Social capital]] is a sociological concept about the value of [[social relation]]s and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use.<ref>{{cite journal|last=Sebastián|first=Valenzuela|author2=Namsu Park |author3=Kerk F. Kee |title=Is There Social Capital in a Social Network Site? Facebook Use and College Students' Life Satisfaction, Trust, and Participation|journal=Journal of Computer-Mediated Communication|year=2009|volume=14|issue=4|pages=875–901|doi=10.1111/j.1083-6101.2009.01474.x|doi-access=free}}</ref><ref>{{cite journal | title=Social Connectivity in America: Changes in Adult Friendship Network Size from 2002 to 2007 |author1=Wang, Hua |author2=Barry Wellman |lastauthoramp=yes | journal=American Behavioral Scientist | year=2010 | volume=53 | issue=8 | pages=1148–1169 | doi=10.1177/0002764209356247|url=https://semanticscholar.org/paper/402068f74efb35c093e7fd8c0f8268521219e242 }}</ref> In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.<ref name="GaudeulGiannetti2013">{{cite journal|last1=Gaudeul|first1=Alexia|last2=Giannetti|first2=Caterina|title=The role of reciprocation in social network formation, with an application to LiveJournal|journal=Social Networks|volume=35|issue=3|year=2013|pages=317–330|issn=03788733|doi=10.1016/j.socnet.2013.03.003}}</ref>
社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。
社会资本是一个关于社会关系的价值、以及合作与信心对取得积极成果的作用的社会学概念。该术语指的是一个人可以从其社会关系中获得的价值。例如，新来的移民可以利用他们与先到的移民的社会关系，获得可能本难以获得的工作（如因为不熟悉当地语言）。社会资本与社会网络使用强度存在正相关关系。在一个动态的框架中，网络中更高的活动会产生更高的社会资本，而社会资本本身又会激励更多的活动。
=== Advertising 广告===
=== Advertising 广告===
This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.
This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.
这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
这个特别的集群侧重于品牌形象和促销策略的有效性，同时考虑到顾客参与对销售和品牌形象的影响。这是通过诸如基于数学领域的研究（如数据挖掘和分析）的情感分析等技术来衡量的。这一研究领域产生了大量的商业应用，因为任何研究的主要目标都是了解消费者行为并推动销售。
===Network position and benefits 网络地位和利益===
===Network position and benefits 网络地位和利益===
In many [[Formal organizations|organizations]], members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities.<ref name="Burt 2004" /> Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist [[John Stuart Mill]], writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress."<ref name=":5">{{cite book|last=Mill|first=John|title=Principles of Political Economy|year=1909|publisher=William J Ashley|location=Library of Economics and Liberty}}</ref> Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.
在许多组织中，成员倾向于把他们的活动集中在他们自己的团队中，这抑制了创造力并限制了机会。一个网络连接结构洞的成员在发现和开发机会方面有优势。<ref name="Burt 2004"/> 这样的成员可以通过充当两个集群之间的信息“中间人”来调动社会资本，否则这些集群就不会有接触，并因此拥有了获取新想法、观点和机会的渠道。英国哲学家和政治经济学家John Stuart Mill写道:“不管怎样……让人们跟与自己不同的人接触....这种沟通是进步的主要来源之一。”<ref name=":5" /> 因此，一个拥有富含结构洞的网络的玩家可以通过新的想法和机会为组织增加价值。这反过来又有助于个人的职业发展和晋升。
A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms.<ref name=":6">{{cite journal|last=Gardner|first=Heidi|author2=Eccles, Robert |title=Eden McCallum: A Network Based Consulting Firm|journal=Harvard Business School Review|year=2011}}</ref> By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.
社会资本中介人还可以从促进联系人之间的信息流动中获得控制权利。在咨询公司 Eden McCallum 的案例中，创始人能够通过与前三大咨询公司顾问和中等规模的行业公司建立联系来发展其事业。<ref name=":6" /> 通过连接结构洞和调动社会资本，玩家可以通过在联系人之间执行新的机会来发展其事业。
There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations.<ref name=":7">Xiao, Zhixing; Tsui, Anne (2007). "When Brokers May Not Work: The Cultural Contingency of [http://iniciarsesionentrar.com/facebook/ Social] Capital in Chinese High-tech Firms". ''Administrative Science Quarterly''.</ref> However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted.<ref name="Burt 2004" /> Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.
有研究证实和反驳了信息中介的好处。Zhixing Xiao 对中国高科技企业的一项研究发现，结构洞的控制效益“与占主导地位的企业合作精神不协调，且因这类组织的共享价值观，信息效益不能实现”。<ref name=":7" /> 然而，这项研究只分析了往往有很强的公共共享价值观的中国企业。结构洞的信息和控制利益对于那些在整个公司层面上不那么具有包容性和合作性的公司来说仍然是有价值的。2004年，罗纳德·伯特 Ronald Burt调查了为美国最大的电子公司之一管理供应链的673名管理者。他发现，那些经常与其他团体讨论问题的管理人员收入更高，得到的工作评价更积极，也更有可能获得晋升。<ref name="Burt 2004" /> 因此，<font color="#32CD32">连接</font>结构洞对组织有益，反过来对个人的职业生涯也有益。
===Social media 社交媒体===
===Social media 社交媒体===
[[Computer network]]s combined with social networking software produces a new medium for social interaction.<ref name=":8">{{Cite book|title=The International Encyclopedia of Media Effects|last=Amichai-Hamburger|first=Yair|last2=Hayat|first2=Tsahi|date=2017|publisher=John Wiley & Sons, Inc.|isbn=9781118783764|language=en|doi=10.1002/9781118783764.wbieme0170}}</ref> A relationship over a computerized [[social networking service]] can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a [[computer mediated communication]] context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of [[electronic commerce]], information exchanged may also correspond to exchanges of money, goods or services in the "real" world.<ref name=":9">{{cite journal | title=Studying Online Social Networks | last1=Garton|first1=Laura|first2=Caroline |last2=Haythornthwaite|author2-link=Caroline Haythornthwaite|first3=Barry|last3=Wellman|author3-link=Barry Wellman | journal=Journal of Computer-Mediated Communication |date=23 June 2006 | volume=3 | issue=1 |pages = 0| doi=10.1111/j.1083-6101.1997.tb00062.x}}</ref> [[Social network analysis]] methods have become essential to examining these types of computer mediated communication.
计算机网络与社交网络软件的结合为社交互动提供了一种新的媒介。<ref name=":8" /> 计算机化的社交网络服务上的关系可以通过上下文、方向和强度来描述。关系的内容指的是交换的资源。在<font color="#ff8000">电脑中介传播（Computer-mediated Communication）</font>环境中，社会对交换不同种类的信息，包括发送数据文件或计算机程序，以及提供情感支持或安排会议。随着<font color="#ff8000">电子商务</font>的兴起，交换的信息也可能对应于“现实”世界中的货币、商品或服务的交换。<ref name=":9" /> 社会网络分析方法已经成为检验此类计算机为媒介的交流的必要手段。
In addition, the sheer size and the volatile nature of [[social media]] has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.<ref name=":10">{{cite journal | last1 = Wei | first1 = Wei | last2 = Joseph | first2 = Kenneth | last3 = Liu | first3 = Huan | last4 = Carley | first4 = Kathleen M. | year = 2016 | title = Exploring Characteristics of Suspended Users and Network Stability on Twitter | url = | journal = Social Network Analysis and Mining | volume = 6 | issue = | page = 51 | doi = 10.1007/s13278-016-0358-5 }}</ref>
此外，社交媒体的庞大规模和不稳定性也催生了新的网络指标。从社交媒体中提取网络的一个关键问题是，在缺失数据的情况下，网络指标缺乏稳健性。<ref name=":10" />
=='''<font color="#32CD32">参阅</font>'''==
* [[Bibliography of sociology]] 社会学参考书目
* [[Bibliography of sociology]] 社会学参考书目
* [[Business networking]] 商业社交
* [[Business networking]] 商业社交
* [[Collective network]] 集体网络
* [[Collective network]] 集体网络
* [[International Network for Social Network Analysis]] 国际社会网络学会
* [[International Network for Social Network Analysis]] 国际社会网络学会
* [[Network society]] 网络社会
* [[Network society]] 网络社会
* [[Network theory]] 网络理论
* [[Network theory]] 网络理论
*[[Network science]] 网络科学
*[[Network science]] 网络科学
* [[Semiotics of social networking]] 社交网络符号学
* [[Semiotics of social networking]] 社交网络符号学
* [[Scientific collaboration network]] 科学合作网络
* [[Scientific collaboration network]] 科学合作网络
* [[Social network analysis]] 社会网络分析
* [[Social network analysis]] 社会网络分析
* [[Social network (sociolinguistics)]] 社会网络
* [[Social network (sociolinguistics)]] 社会网络
* [[Social networking service]] 社会网络服务
* [[Social networking service]] 社会网络服务
* [[Social web]] 社交网络
* [[Social web]] 社交网络
* [[Structural fold]] 结构折叠
* [[Structural fold]] 结构折叠
==参考文献==
==References 参考资料==
==Further reading 进一步阅读==
==拓展阅读==
* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}
* {{cite journal |last1=Aneja |first1=Nagender |last2=Gambhir |first2=Sapna |title=Ad-hoc Social Network: A Comprehensive Survey |url=http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf |journal=International Journal of Scientific & Engineering Research |volume=4 |issue=8 |date=August 2013 |pages=156–160|issn=2229-5518}}
* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}
* {{cite book |last=Barabási |first=Albert-László |year=2003 |title=Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life |publisher=Plum |isbn=978-0-452-28439-5 |url-access=registration |url=https://archive.org/details/linkedhoweveryth00bara }}
* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}
* {{cite book |last=Barnett |first=George A. |year=2011 |title=Encyclopedia of Social Networks |publisher=Sage |isbn=978-1-4129-7911-5}}
* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}
* {{cite book |last=Estrada |first=E |year=2011 |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |isbn=978-0-199-59175-6}}
* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}
* {{cite book |last=Ferguson |first=Niall |year=2018 |title=The Square and the Tower: Networks and Power, from the Freemasons to Facebook |isbn=978-0735222915 |publisher=Penguin Press}}
* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}
* {{cite book |last=Freeman |first=Linton C. |year=2004 |title=The Development of Social Network Analysis: A Study in the Sociology of Science |publisher=Empirical Press |isbn=978-1-59457-714-7}}
* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}
* {{cite book |last=Kadushin |first=Charles |year=2012 |title=Understanding Social Networks: Theories, Concepts, and Findings |location= |publisher=Oxford University Press |isbn=978-0-19-537946-4}}
* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}
* {{cite book |last1=Mauro |first1=Rios |last2=Petrella |first2=Carlos |year=2014 |title=La Quimera de las Redes Sociales |trans-title=The Chimera of Social Networks |language=Spanish |publisher=Bubok España |isbn=978-9974-99-637-3}}
* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}
* {{cite book |last1=Rainie |first1=Lee |last2=Wellman |first2=Barry |year=2012 |title=Networked: The New Social Operating System |url=https://archive.org/details/networkednewsoci0000rain |url-access=registration |location=Cambridge, Mass. |publisher=MIT Press |isbn=978-0262017190}}
* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}
* {{cite book |last=Scott |first=John |year=1991 |title=Social Network Analysis: A Handbook |publisher=Sage |isbn=978-0-7619-6338-7}}
* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}
* {{cite book |last1=Wasserman |first1=Stanley |last2=Faust |first2=Katherine |year=1994 |title=Social Network Analysis: Methods and Applications |publisher=Cambridge University Press |isbn=978-0-521-38269-4 |series=Structural Analysis in the Social Sciences }}
* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}
* {{cite book |last1=Wellman |first1=Barry |last2=Berkowitz |first2=S. D. |year=1988 |title=Social Structures: A Network Approach |series=Structural Analysis in the Social Sciences |publisher=Cambridge University Press |isbn=978-0-521-24441-1}}
==外部链接==
===组织===
==External links 外部链接==
* [http://www.insna.org/ International Network for Social Network Analysis]
* [http://www.insna.org/ International Network for Social Network Analysis]
===同行评审期刊===
===Peer-reviewed journals 同行评审期刊===
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''
* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''
* ''[http://journals.cambridge.org/action/displaySpecialPage?pageId=3656 Network Science]''
* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''
* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''
* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''
* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''
* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''
* ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]''
* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}
* {{cite journal |title=INSNA - ''Connections'' Journal |journal=Connections Bulletin of the International Network for Social Network Analysis |location=Toronto |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |issn=0226-1766 |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}
===课本及教育资源===
===Textbooks and educational resources 课本及教育资源===
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg
* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle
* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle
* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti
* [http://www.analytictech.com/networks/ Social Network Analysis Instructional Web Site] by S. Borgatti
* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)
* ''[https://web.archive.org/web/20160304063126/http://www.novagob.org/file/view/153259/ebook-gratuito-sobre-redes-sociales-virtuales Guide for virtual social networks for public administrations]'' (2015) by Mauro D. Ríos (in Spanish)
===数据集===
===Data sets 数据集===
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]
* [http://pajek.imfm.si/doku.php?id=data:urls:index Pajek's list of lists of datasets]
* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]
* [http://networkdata.ics.uci.edu/index.html UC Irvine Network Data Repository]
* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]
* [http://snap.stanford.edu/data/ Stanford Large Network Dataset Collection]
* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]
* [http://www-personal.umich.edu/~mejn/netdata/ M.E.J. Newman datasets]
* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]
* [http://vlado.fmf.uni-lj.si/pub/networks/data/ Pajek datasets]
* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]
* [http://wiki.gephi.org/index.php?title=Datasets#Social_networks Gephi datasets]
* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]
* [http://konect.uni-koblenz.de/networks KONECT – Koblenz network collection]
* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]
* [http://www.stats.ox.ac.uk/~snijders/siena/ RSiena datasets]
==编者推荐==
==编者推荐==
[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。
[http://arxiv.org/abs/2002.09485 The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools]：近年来，基于社交网络的应用呈指数级增长，其中一个方面的原因是，这个应用领域提供了一个特别肥沃的地方，可以测试和开发最先进的计算技术，从 Web 中提取有价值的信息。这项工作的主要贡献有三个方面：(1) 我们对社会网络分析 (SNA) 的最新发展水平进行了文献综述；(2) 我们提出了一套基于 SNA 四个基本特征（或维度）的新度量标准；(3) 最后，我们对一套流行的 SNA 工具和框架进行了定量分析。我们还进行了一项科学计量学研究，以检测该领域中最活跃的研究领域和应用领域。本工作提出了四个不同维度的定义，即(a)模式、(b)知识发现、(c)信息融合、(d)集成、可伸缩性和可视化，用于定义一组新的度量（称为度），以评估 SNA 的不同软件工具和框架（根据之前的度量对一组 20 个 SNA-软件工具进行分析和排序）。这些维度连同定义的程度，可以评价和衡量社会网络技术的成熟度，寻求对它们的定量评估，从而揭示这一活跃领域的挑战和未来发展趋势。