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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.

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.

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="#FFD700">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)和曼彻斯特学院(Manchester School)有关的社会人类学家，经常被认为是进行了一些最早的野。在社会学方面，塔尔科特 · 帕森斯早期(1930年代)的著作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 · 布劳的社会交换理论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="#FFD700">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="#FFD700">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 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.

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.

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 Charles Tilly，他专注于政治和社区社会学和社会运动的网络，还有 Stanley Milgram，他发表了六度分隔理论的论文。马克·格兰诺维特和 Barry Wellman 是 White 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.

==Levels of analysis==

==Levels of analysis 分析水平==

[[File:Network self-organization stages.png|thumb|right|Self-organization of a network, based on Nagler, Levina, & Timme, (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|Self-organization of a network, based on Nagler, Levina, & Timme, (2011) 图三：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:Social Network Diagram (large).svg|right|thumb|Centrality]]

[[File:Social Network Diagram (large).svg|right|thumb|Centrality]]

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.

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.

一般来说，社会网络是自组织的、涌现的和复杂的，以至于从组成系统的元素的局部相互作用中出现了一个全局连贯的模式。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，例如，世界上所有的人际关系是不可行的，可能包含太多的信息，以至于没有提供信息。计算能力的实际限制、道德规范以及参与者的招募和支付也限制了社会网络分析的范围。本地系统的细微差别在大型网络分析中可能会丢失，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，社会网络被分析在与研究者的理论问题相关的尺度上。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: 微观层次、中观层次和宏观层次。

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募和支付也限制了社会网络分析的范围。本地系统的细微差别在大型网络分析中可能会丢失，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，社会网络被分析在与研究者的理论问题相关的尺度上。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。

===Micro level===

===Micro level 微观层面===

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.

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.

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.

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.

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.

二元层次: 二元是两个人之间的社会关系。网络对二元关系的研究可以集中在关系的结构上。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 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.

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.

三元等级: 把一个人加到一个二元组中，你就有了一个三元组。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 / 互惠的倾向。在弗里茨 · 海德的平衡理论中，三元组是社会动力学的关键。争强好胜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合会建立起来的。本文利用符号图理论进行了研究。

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, 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.

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.

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.

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.

===Meso level===

===Meso level 中观层面===

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>

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.

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.

[[File:Social Red.jpg|thumb|right|Social network diagram, meso-level]]

[[File:Social Red.jpg|thumb|right|Social network diagram, meso-level 图四：中观层而社会网络图]]

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.

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.

组织: 正式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、特许经营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-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.

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.

随机分布的网络: 社会网络的指数随机图模型在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="#FFD700">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。

[[File:Scale-free network sample.png|thumb|right|Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).]]

[[File:Scale-free network sample.png|thumb|right|Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).图五：一个随机网络和一个无尺度网络的例子。每个图有32个顶点和32条边。注意无标度图中的“集线器”（阴影部分）（右侧）。]]

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.

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.

无标度网络: 无尺度网络网络是一个度分布遵循幂律的网络，至少是渐近的。在网络理论中，无标度理想网络是一个具有度分布的随机网络，它揭示了社会群体的规模分布。无标度网络的具体特征随创建无标度网络的理论和分析工具的不同而不同，然而，一般来说，无标度网络具有一些共同的特征。无尺度网络的一个显著特征是，度大大超过平均值的顶点具有相对的共性。最高度的节点通常被称为“枢纽” ，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普遍特征是集聚系数分布，它随着节点度的增加而减少。这个分布也遵循一个幂定律。上面显示的网络演化的 barab si 模型就是无尺度网络的一个例子。

'''<font color="#FFD700">无标度网络 A Scale-free Network</font>''': 无标度网络网络是一个'''<font color="#FFD700">度分布  Degree Distribution</font>'''遵循幂律的网络，至少是渐近的。在网络理论中，无标度理想网络是一个具有度分布的随机网络，它揭示了社会群体的规模分布。无标度网络的具体特征随创建无标度网络的理论和分析工具的不同而不同，然而，一般来说，无标度网络具有一些共同的特征。无尺度网络的一个显著特征是，度大大超过平均值的顶点具有相对的共性。最高度的节点通常被称为“枢纽” ，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普遍特征是集聚系数分布，它随着节点度的增加而减少。这个分布也遵循一个幂定律。上面显示的网络演化的 barab si 模型就是无尺度网络的一个例子。

===Macro level===

===Macro level 宏观层面===

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]].

==Theoretical links==

==Theoretical links 理论联系==

===Imported theories===

===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>

===Indigenous theories===

===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]].

==Structural holes==

==Structural holes 结构性漏洞==

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"/>

==Research clusters==

==Research clusters 研究集群==

===Communication===

===Communication 沟通===

[[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.

===Community===

===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.

===Complex networks===

===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]].

===Criminal networks===

===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.

===Diffusion of innovations===

===Diffusion of innovations 创新产品渗透理论===

[[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.

===Demography===

===Demography 人口统计===

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.

===Economic sociology===

===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>

===Health care===

===Health care 医疗保健===

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>

===Human ecology===

===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>

===Language and linguistics===

===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.

===Literary networks===

===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.

===Organizational studies===

===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>

===Social capital===

===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>