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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}}

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}}

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.

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="#FFD700">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="#FFD700">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。

'''<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 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.

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="#FFD700">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。

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。

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.

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="#FFD700">人类学 Anthropology</font>'''、生物学、'''<font color="#FFD700">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="#FFD700">社会语言学 Sociolinguistics</font>'''。

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>'''。

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。

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。

==Levels of analysis 分析水平==

==Levels of analysis 分析层次==

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，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。

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。

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'''<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。

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 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.

'''<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。

'''<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 interactions over a large population.

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.

[[File:Diagram of a social network.jpg|thumb|right|Diagram: section of a large-scale social network]]

[[File:Diagram of a social network.jpg|thumb|right|Diagram: section of a large-scale social network 图六: 一大型社会网络局部]]

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).

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).

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被广泛使用(见大比例尺网络地图)。

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 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.

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.

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，包括网络拓扑的大量非琐碎特征，以及元素之间的复杂连接模式，这些元素既不是纯规则的也不是纯随机的(见复杂性科学、动力系统和混沌理论) ，生物学和技术网络也是如此。这些复杂的网络特征包括程度分布的重尾、高集聚系数、顶点之间的协调性或不协调性、社区结构(见随机块模型)和层次结构。在代理导向网络的情况下，这些特征还包括互惠性、三重显著性特征(TSP，见网络主题)和其他特征。相比之下，许多过去研究过的网络数学模型，如格和随机图，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、三重显著性特征（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。

 

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.

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.

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".

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".

==Structural holes 结构性漏洞==

==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. 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.

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.

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.

富含结构性漏洞的网络是社会资本的一种形式，因为它们提供信息利益。桥接结构漏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，在商业网络中，这对个人的职业生涯是有益的，因为如果他的关系网涵盖不同行业 / 部门的广泛联系，他更有可能听到职位空缺和机会。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。

===Communication 沟通===

===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.

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.