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===中观层面===

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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>

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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 name=":36">{{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>

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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。<ref name=":36" />

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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。

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'''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>

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'''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 name=":37">{{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>

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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。

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'''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>

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随机分布的网络: 社会网络的'''指数随机图模型 Exponential random graph models'''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。

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组织: 正规组织是为共同目标分配任务的社会团体。<ref name="Riketta, M. 2007" /> 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。<ref name="Riketta, M. 2007" /> 网络群体的在线实验证明了通过各种干预优化群体层面协调的方法，这其中包括在群体中加入自主代理。<ref name=":37" />

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'''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 name=":38">{{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>

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[[File:Scale-free network sample.png|thumb|right|图5：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).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）。]]

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随机分布的网络: 社会网络的社会网络的指数随机图模型（Exponential random graph models）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。<ref name=":38" />[[File:Scale-free network sample.png|thumb|right|图5：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).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）。]]

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'''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 name=":39">{{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 name=":40">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.

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无标度网络（A Scale-free Network）：无标度网络网络是一个度分布（Degree Distribution）遵循幂律（Power Law）(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。<ref name=":39" /> 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“枢纽（Hubs）” ，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聚集系数（General Characteristic）分布，它随着节点度的增加而减少。该分布也遵循幂律。<ref name=":40" /> 上述的网络演化的巴拉巴西模型就是无标度网络的一个例子。

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'''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.

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'''[[无标度网络]] A Scale-free Network''': 无标度网络网络是一个'''度分布 Degree Distribution'''遵循'''幂律 Power Law'''(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“'''枢纽 Hubs'''” ，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'''集聚系数 General Characteristic'''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。

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'''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>

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'''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 name=":41">{{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>

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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 Network Topology'''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'''同配性 Assortativity'''或非同配性、社区结构（见'''随机分块模型 Stochastic Block Model'''）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'''随机图 Random Graph'''，并没有表现出这些特征。

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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 Network Topology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、顶点之间的同配性（Assortativity）或非同配性、社区结构（见随机分块模型 Stochastic Block Model）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，如格和随机图（Random Graph），并没有表现出这些特征。<ref name=":41" />

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=== 输入的理论 ===

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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>

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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 name=":42">{{cite book |last1=Kilduff|first1=M.|last2=Tsai|first2=W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref>

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~~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。~~

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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、平衡理论、社会比较论，以及最近的社会认同方法。<ref name=":42" />

===本土理论===

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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。

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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>

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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 name=":43">{{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>

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异质性理论的基础是在一项研究中发现，更多的'''弱关系 Weak Tie'''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”。

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异质性理论的基础是在一项研究中发现，更多的弱关系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”。<ref name=":43" />

==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" />

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~~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。~~

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在网络的背景下，社会资本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。<ref name="Burt 2004" /> 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。<ref name="Burt 1992" />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结构洞。<ref name="Burt 1992" /> 因此，一个连接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。<ref name="Burt 1992" />

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.

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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。

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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。<ref name="Burt 1992" /> 例如，在商业网络中，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。

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==研究集群==

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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.

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~~'''双相演化理论 Dual~~-phase ~~Evolution'''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。~~

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双相演化理论（Dual-phase Evolution）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。

===犯罪网络===

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[[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.

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~~'''创新扩散理论 Diffusion~~ of Innovations'''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。

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创新扩散理论（Diffusion of Innovations）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。

===人口学===

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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.

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在'''人口学 Demography'''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。

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在人口学方面，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。

===经济社会学===

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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>

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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 name=":44">{{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>

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~~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。

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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，经济社会学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。<ref name=":44" />

===卫生保健===

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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>

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社会网络分析越来越多地被纳入卫生保健分析——不仅在'''流行病学 Epidemiology'''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。

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社会网络分析越来越多地被纳入卫生保健分析——不仅在流行病学研究中，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。

===人类生态学===

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[[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>

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[[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 name=":45">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 name=":46">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>

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~~'''~~<~~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。~~

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人类生态学是研究人类与其自然环境、社会环境和环境建构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。<ref name=":45" /><ref name=":46" />

===语言与语言学===

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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.

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语言学和语言学的研究，特别是'''演化语言学 Evolutionary Linguistics'''，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。

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语言学和语言学的研究，特别是进化语言学（Evolutionary Linguistics），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。

===文学网络===

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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.

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In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,<ref name=":47">{{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 name=":48">{{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 name=":49">{{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.

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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。

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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）<ref name=":47" /> 、努伊（De Nooy）<ref name=":48" /> 和Senekal<ref name=":49" /> 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。

===组织研究===

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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>

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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 name=":50">{{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 name=":51">{{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 name=":52">{{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>

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研究正式或非正式组织关系、组织传播、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。<ref name=":50" />研究发现，组织内网络对组织承诺（Organizational Commitment）<ref name=":51" /> 、组织认同<ref name="Jone11" /> 、人际公民行为有影响。<ref name=":52" />

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研究正式或非正式组织关系、'''组织沟通 Organizational Communication'''、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'''组织承诺 Organizational Commitment'''、组织认同、人际公民行为有影响。

=== 社会资本 ===

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[[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>

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[[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 name=":53">(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 name=":54">(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 name=":55">(Claridge, 2018).</ref>

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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。

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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。<ref name=":53" />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。<ref name=":54" /> 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。<ref name=":55" />

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[[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>

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[[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 name=":56">{{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 name=":57">{{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>

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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。

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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。<ref name=":56" /><ref name=":57" /> 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。<ref name="GaudeulGiannetti2013" />

=== 广告===

第310行：第287行：

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.

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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，连接结构洞对组织有益，反过来对个人的职业生涯也有益。~~

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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，连接结构洞对组织有益，反过来对个人的职业生涯也有益。

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[[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.

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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。在电脑中介传播（Computer~~-mediated Communication）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。随着电子商务的兴起，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。

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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。在电脑中介传播（computer-mediated communication）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。随着电子商务的兴起，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>

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