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第1行： − 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。+ − + 第11行：
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第75行： − Network Closure: A measure of the completeness of relational triads. An individual's assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.+ − 网络封闭性: 关系三元组完备性的度量。个人的网络封闭假设(即。他们的朋友也是朋友)被称为及物性。及物性是认知封闭需要的个体或情景特征的产物。+ 第83行：
第83行： − Propinquity: The tendency for actors to have more ties with geographically close others.+ − 亲近性: 行为者倾向于与地理上相近的其他人有更多的联系。+ − − [[Bridge (graph theory)|Bridge]]: An individual whose weak ties fill a [[Structural holes|structural hole]], providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.<ref name="Granovetter, M. 1973 1360–1380">{{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|url=https://semanticscholar.org/paper/c9aece346139711b8c65c618da99cdbecb162575 }}</ref> − − Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure. − − 桥梁: 一个人的弱关系填补了一个结构上的空洞，提供了两个人或群体之间唯一的联系。它还包括当较长的路线由于消息失真或传递失败的高风险而不可行时的最短路线。 − − − − [[Centrality]]: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network.<ref>{{cite book|author=Hansen, Derek|title=Analyzing Social Media Networks with NodeXL|publisher=Morgan Kaufmann|year=2010|isbn=978-0-12-382229-1|page=32|url=https://books.google.com/books?id=rbxPm93PRY8C&pg=PA32|display-authors=etal}}</ref><ref>{{cite book|author=Liu, Bing|title=Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data|publisher=Springer|year=2011|isbn=978-3-642-19459-7|page=271|url=https://books.google.com/books?id=jnCi0Cq1YVkC&pg=PA271}}</ref><ref>{{cite book|author=Hanneman, Robert A.|author2=Riddle, Mark|lastauthoramp=y|chapter=Concepts and Measures for Basic Network Analysis|title=The Sage Handbook of Social Network Analysis|publisher=SAGE|year=2011|isbn=978-1-84787-395-8|pages=364–367|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA364}}</ref><ref>{{cite book|author1=Tsvetovat, Maksim |author2=Kouznetsov, Alexander|lastauthoramp=y|title=Social Network Analysis for Startups: Finding Connections on the Social Web|publisher=O'Reilly|year=2011|isbn=978-1-4493-1762-1|page=45|url=https://books.google.com/books?id=hVOxjkoLSiEC&pg=PA45}}</ref> Examples of common methods of measuring "centrality" include [[betweenness centrality]],<ref name="comprehensive"/> [[closeness centrality]], [[eigenvector centrality]], [[alpha centrality]], and [[degree centrality]].<ref>{{cite journal | last1 = Opsahl | first1 = Tore | last2 = Agneessens | first2 = Filip | last3 = Skvoretz | first3 = John | title = Node centrality in weighted networks: Generalizing degree and shortest paths | doi = 10.1016/j.socnet.2010.03.006 | year = 2010 | pages = 245–251 | volume = 32 | journal = Social Networks | url=http://toreopsahl.com/2010/04/21/article-node-centrality-in-weighted-networks-generalizing-degree-and-shortest-paths/ | issue = 3 }}</ref> − − Centrality: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network. Examples of common methods of measuring "centrality" include betweenness centrality, − − 中心性: 中心性是指一组度量标准，旨在量化网络中特定节点(或组)的“重要性”或“影响力”(在各种意义上)。常见的测量“中心性”的方法包括介于中心性、, − − − − [[Dense graph|Density]]: The proportion of direct ties in a network relative to the total number possible.<ref>{{cite book|chapter=Social Network Analysis|title=Field Manual 3-24: Counterinsurgency|publisher=Headquarters, [[Department of the Army]]|pages=B–11 – B–12|chapter-url=https://fas.org/irp/doddir/army/fm3-24.pdf}}</ref><ref>{{cite book|author=Xu, Guandong |title=Web Mining and Social Networking: Techniques and Applications|publisher=Springer|year=2010|isbn=978-1-4419-7734-2|page=25|url=https://books.google.com/books?id=mXo9zKeYa6cC&pg=PA25|display-authors=etal}}</ref> − − Density: The proportion of direct ties in a network relative to the total number possible. − − 密度: 网络中直接联系在可能总数中所占的比例。 − − − − Distance: The minimum number of ties required to connect two particular actors, as popularized by [[Stanley Milgram]]'s [[small world experiment]] and the idea of 'six degrees of separation'. 第121行：
第95行： + − Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an [[entrepreneur]] a competitive advantage. This concept was developed by sociologist [[Ronald Stuart Burt|Ronald Burt]], and is sometimes referred to as an alternate conception of social capital. 第129行：
第103行： + − Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).<ref name="Granovetter, M. 1973 1360–1380"/> Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges. − + − + − + − − ===Segmentation=== − − Groups are identified as '[[clique]]s' if every individual is directly tied to every other individual, '[[social circle]]s' if there is less stringency of direct contact, which is imprecise, or as [[Structural cohesion|structurally cohesive]] blocks if precision is wanted.<ref name="uci"/> − − Groups are identified as 'cliques' if every individual is directly tied to every other individual, 'social circles' if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. − − 如果每个个体都与其他个体直接联系在一起，那么群体就被定义为小集团; 如果直接联系不那么紧密，那么群体就被定义为社交圈; 如果需要精确性，群体就被定义为结构上有凝聚力的集团。 − − − − − − Clustering coefficient: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'. − − 集聚系数: 一个节点的两个关联是关联的可能性的度量。更高的集聚系数表明更大的集团主义。 − − Cohesion: The degree to which actors are connected directly to each other by [[Social cohesion|cohesive bonds]]. [[Structural cohesion]] refers to the minimum number of members who, if removed from a group, would disconnect the group.<ref name="asanet"/><ref>{{cite book|author=Pattillo, Jeffrey|chapter=Clique relaxation models in social network analysis|editor1=Thai, My T.|editor2=Pardalos, Panos M.|lastauthoramp=y|title=Handbook of Optimization in Complex Networks: Communication and Social Networks|publisher=Springer|year=2011|isbn=978-1-4614-0856-7|page=149|chapter-url=https://books.google.com/books?id=bdRdcHxQQLQC&pg=PA149|display-authors=etal}}</ref> 第163行：
第119行： + − ==Modelling and visualization of networks== − Visual representation of social networks is important to understand the network data and convey the result of the analysis.<ref>{{cite journal|url=http://www.cmu.edu/joss/content/articles/volume1/Freeman.html|author=Linton C. Freeman|title=Visualizing Social Networks|journal=Journal of Social Structure|volume=1}}</ref> Numerous methods of visualization for data produced by social network analysis have been presented.<ref>{{cite journal|last=Hamdaqa|first=Mohammad |author2=Tahvildari, Ladan |author3=LaChapelle, Neil |author4=Campbell, Brian|title=Cultural Scene Detection Using Reverse Louvain Optimization|journal=Science of Computer Programming|date=2014|doi=10.1016/j.scico.2014.01.006|volume=95|pages=44–72|url=https://zenodo.org/record/889712 }}</ref><ref>{{cite conference|author=Bacher, R.|year=1995|title=Graphical Interaction and Visualization for the Analysis and Interpretation of Contingency Analysis Result|conference=Proceedings of the 1995 Power Industry Computer Applications|pages=128–134|location=Salt Lake City, USA|publisher=IEEE Power Engineering Society|doi=10.1109/PICA.1995.515175}}</ref><ref>{{cite journal | last1 = Caschera | first1 = M. C. | last2 = Ferri | first2 = F. | last3 = Grifoni | first3 = P. | year = 2008 | title = SIM: A dynamic multidimensional visualization method for social networks | url = | journal = PsychNology Journal | volume = 6 | issue = 3| pages = 291–320 }}</ref> Many of the [[Social network analysis software|analytic software]] have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.<ref name="interpreting"/>+ − 社会网络的可视化表示对于理解网络数据和传达分析结果具有重要意义。对于社会网络分析产生的数据，已经提出了许多可视化的方法。许多分析软件都有网络可视化模块。通过以各种布局显示节点和关系，并将颜色、大小和其他高级属性归属于节点，可以对数据进行探索。网络的可视化表示可能是传递复杂信息的一种强有力的方法，但是在仅仅从可视化显示中解释节点和图形属性时应该注意，因为它们可能通过定量分析更好地表达结构属性。+ − [[Signed graph]]s can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a [[Cycle (graph theory)|cycle]] where the product of all the signs are positive. According to [[balance theory]], balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balanced and unbalanced cycles, the evolution of signed [[social network graph]]s can be predicted.<ref>{{cite journal|author=Cartwright, D. |author2=Frank Harary|year=1956|url=http://snap.stanford.edu/class/cs224w-readings/cartwright56balance.pdf|title=Structural balance: a generalization of Heider's theory|journal=[[Psychological Review]]|volume=63|issue=5|pages=277–293|doi=10.1037/h0046049|pmid=13359597|author2-link=Frank Harary}} Link from [[Stanford University]].+ − − Signed graphs can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a cycle where the product of all the signs are positive. According to balance theory, balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balanced and unbalanced cycles, the evolution of signed social network graphs can be predicted.<ref> Link from Stanford University. − − 符号图可以用来说明人与人之间的良好关系和不良关系。两个节点之间的正边表示正关系(友谊、联盟、约会) ，负边表示负关系(仇恨、愤怒)。符号社会网络图可以用来预测图的未来发展。在有符号的社交网络中，存在着“平衡”和“不平衡”的循环概念。一个平衡的循环被定义为所有迹象的结果都是正数的循环。根据平衡理论，平衡图表示的是一群不太可能改变自己对团队中其他人的看法的人。不平衡的图表代表了一群人，他们很可能改变他们对自己群体中人的看法。例如，一个由3个人组成的群体(a，b，c) ，其中 a 和 b 具有正相关关系，b 和 c 具有正相关关系，而 c 和 a 具有负相关关系则是一个不平衡的循环。这个群很可能演变成一个平衡的循环，例如 b 只和 a 有很好的关系，而 a 和 b 都和 c 有负的关系。利用平衡和不平衡循环的概念，可以预测符号社会网络图的演化。 参考链接来自斯坦福大学。 − </ref>+ − </ref>+ − / 参考 − − − − Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * [[Net-map toolbox]]. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.<ref name="visualizing"/> − − Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * Net-map toolbox. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected. − − 特别是在使用社会网络分析作为促进变革的工具时，参与式网络制图的不同方法已被证明是有用的。在这里，参与者 / 面试者在数据收集会议期间通过实际绘制网络(用笔和纸或数字)来提供网络数据。笔和纸的网络映射方法的一个例子是 * Net-map 工具箱，它还包括一些参与者属性的集合(感知的影响力和参与者的目标)。这种方法的一个好处是，它允许研究人员收集定性数据，并在收集网络数据时提出澄清问题。 − − − − ===Social networking potential=== − − {{Cleanup|section|reason=More careful cleanup after merge required|date=December 2015}} − − − − Social Networking Potential (SNP) is a numeric [[coefficient]], derived through [[algorithm]]s<ref>e.g., Anger, I., & Kittl, C. (2011, September). [http://www.l2f.inesc-id.pt/~fmmb/wiki/uploads/Work/misnis.ref07.pdf Measuring influence on Twitter]. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies (p. 31). ACM.</ref><ref>Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management. 52, p. 949-975.</ref> to represent both the size of an individual's [[social network]] and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is [[Social marketing intelligence#Alpha users|Alpha User]], defined as a person with a high SNP. − − Social Networking Potential (SNP) is a numeric coefficient, derived through algorithms to represent both the size of an individual's social network and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is Alpha User, defined as a person with a high SNP. − − 社交网络潜力(SNP)是一个数值系数，通过算法来表示个人社交网络的规模和他们影响该网络的能力。Snp 系数是由 Bob Gerstley 于2002年首次定义和使用的。一个密切相关的术语是 Alpha User，定义为具有高 SNP 的人。 + − + − Snp 系数有两个主要功能:+ − #The [[categorization|classification]] of individuals based on their social networking potential, and+ − The classification of individuals based on their social networking potential, and+ − 基于社交网络潜力的个人分类，以及 − #The weighting of [[wikt:respondent|respondents]] in quantitative [[marketing research]] studies. 第229行：
第157行： + − By calculating the SNP of respondents and by [[Behavioral targeting|targeting]] High SNP respondents, the [[Persuasion|strength]] and [[relevance]] of quantitative marketing research used to drive [[viral marketing]] strategies is enhanced. 第237行：
第165行： + − [[Variable (research)|Variables]] used to calculate an individual's SNP include but are not limited to: participation in Social Networking activities, group memberships, leadership roles, recognition, publication/editing/contributing to non-electronic media, publication/editing/contributing to electronic media (websites, blogs), and frequency of past distribution of information within their network. The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See [[Viral Marketing]].<ref>{{cite book|last1=(Hrsg.)|first1=Sara Rosengren|title=The Changing Roles of Advertising|date=2013|publisher=Springer Fachmedien Wiesbaden GmbH|location=Wiesbaden|isbn=9783658023645|url=https://www.springer.com/us/book/9783658023645|accessdate=22 October 2015}}</ref> − + + + − + − The first book to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book to discuss Alpha Users more generally in the context of social marketing intelligence was Communities Dominate Brands by Ahonen & Moore in 2005. In 2012, Nicola Greco (UCL) presents at TEDx the Social Networking Potential as a parallelism to the potential energy that users generate and companies should use, stating that "SNP is the new asset that every company should aim to have".+ − 第一本讨论阿尔法用户在移动通信受众中的商业应用的书是 Ahonen，Kasper 和 Melkko 在2004年出版的3 g 营销。第一本在社会营销情报的背景下更广泛地讨论 Alpha 用户的书是 Ahonen & Moore 在2005年出版的《社区主导品牌》。2012年，尼古拉 · 格雷科(UCL)在 TEDx 上发表了《社交网络的潜力》(the Social Networking Potential) ，并将其与用户产生的潜在能源和公司应该使用的能源进行了比较，指出“ SNP 是每个公司都应该致力于拥有的新资产”。 + − ==Practical applications==+ − {{see also|Social network analysis (criminology)}} − Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and [[data mining|mining]], network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, [[social sharing]] and filtering, [[recommender system]]s development, and [[link prediction]] and entity resolution.<ref name="Golbeck" /> In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, [[information system]] development analysis,<ref>{{cite journal|last1=Aram|first1=Michael|last2=Neumann|first2=Gustaf|title=Multilayered analysis of co-development of business information systems|journal=Journal of Internet Services and Applications|date=2015-07-01|volume=6|issue=1|doi=10.1186/s13174-015-0030-8|url=http://www.jisajournal.com/content/pdf/s13174-015-0030-8.pdf}}</ref> marketing, and [[business intelligence]] needs (see [[social media analytics]]). Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and [[media use]], and [[Collaborative problem-solving group|community-based problem solving]]. − + + − === Security applications === − Social network analysis is also used in intelligence, [[counter-intelligence]] and [[law enforcement]] activities. This technique allows the analysts to map covert organizations such as an [[espionage]] ring, an organized crime family or a street gang. The [[National Security Agency]] (NSA) uses its [[clandestine operation|clandestine]] [[mass surveillance|mass]] [[computer surveillance|electronic surveillance]] programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis.<ref name="nsa_degrees">{{cite news|url=https://www.theguardian.com/world/2013/jul/17/nsa-surveillance-house-hearing|date=17 July 2013|accessdate=19 July 2013|title=NSA warned to rein in surveillance as agency reveals even greater scope|newspaper=The Guardian|last1=Ackerman|first1=Spencer}}</ref> After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network.<ref name="nsa_how">{{cite web|url=http://www.digitaltonto.com/2013/how-the-nsa-uses-social-network-analysis-to-map-terrorist-networks/|date=12 June 2013|accessdate=19 Jul 2013|title=How The NSA Uses Social Network Analysis To Map Terrorist Networks}}</ref> This allows military or law enforcement assets to launch capture-or-kill [[decapitation attack]]s on the [[high-value targets]] in leadership positions to disrupt the functioning of the network.+ − + − 社会网络分析也用于情报、反情报和执法活动。这种技术使分析师能够描绘出隐蔽的组织，如间谍网、有组织犯罪家族或街头帮派。美国国家安全局利用其秘密的大规模电子监视方案，生成对恐怖分子基层组织和其他被认为与国家安全有关的网络进行此类分析所需的数据。在这次网络分析中，国家安全局查找了三个深层节点。在社会网络的初始映射完成后，进行分析以确定网络的结构并确定，例如，网络中的领导者。这使得军事或执法资产能够对处于领导地位的高价值目标发动抓捕或杀死斩首攻击，以破坏网络的运作。+ − The NSA has been performing social network analysis on [[call detail record]]s (CDRs), also known as [[metadata]], since shortly after the [[September 11 attacks]].<ref name="NSA_SNA">{{cite journal|url=https://www.wired.com/science/discoveries/news/2006/05/70888|title=NSA Using Social Network Analysis|journal=Wired|date=12 May 2006|accessdate=19 July 2013}}</ref><ref name="nsa_usa">{{cite web|url=http://www.slate.com/articles/news_and_politics/explainer/2006/05/how_the_nsa_does_social_network_analysis.html|date=11 May 2006|accessdate=19 July 2013|title=NSA has massive database of Americans' phone calls }}</ref>+ − 自从911袭击发生后不久，美国国家安全局就一直在对通话细节记录(cdr)(也称为元数据)进行社交网络分析。+ − === Textual analysis applications ===+ − − Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.<ref>{{cite journal|vauthors=Sudhahar S, De Fazio G, Franzosi R, Cristianini N|year=2013|title=Network analysis of narrative content in large corpora|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|doi=10.1017/S1351324913000247|url=https://research-information.bristol.ac.uk/en/publications/network-analysis-of-narrative-content-in-large-corpora(dfb87140-42e2-486a-91d5-55f9007042df).html|hdl=1983/dfb87140-42e2-486a-91d5-55f9007042df}}</ref> This automates the approach introduced by Quantitative Narrative Analysis,<ref>Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA" /> − Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by Quantitative Narrative Analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object. − 大型文本语料库可以转化为网络，然后用社会网络分析的方法进行分析。在这些网络中，节点是社会参与者，链接是行动。这些网络的提取可以通过使用解析器实现自动化。由此产生的网络可以包含数千个节点，然后利用网络理论中的工具对其进行分析，以确定关键参与者、关键群体或党派，以及总体网络的健壮性或结构稳定性或某些节点的中心性等一般性质。这使定量叙事分析引入的方法自动化，其中主语-动词-宾语三联体被认为是由动作连接的一对行为者，或者由行为者-宾语形成的一对行为者。 − [[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal|title=Automated analysis of the US presidential elections using Big Data and network analysis|vauthors=Sudhahar S, Veltri GA, Cristianini N|journal=Big Data & Society|volume=2|issue=1|pages=1–28|year=2015|doi=10.1177/2053951715572916}}</ref>]]+ − Narrative network of US Elections 2012+ − 2012年美国大选叙事网络+ − − In other approaches, textual analysis is carried out considering the network of words co-occurring in a text (see for example the [[Semantic Brand Score]]). In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range). − 在其他方法中，考虑到词汇网络在文本中的同时出现(例如语义品牌得分) ，进行文本分析。在这些网络中，节点是单词，它们之间的链接根据共现频率(在一个特定的最大范围内)进行加权。+ + + − === Internet applications === − Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites.<ref name=Ghanbarnejad/> [[Hyperlink]] analysis can be used to analyze the connections between [[website]]s or [[Web page|webpages]] to examine how information flows as individuals navigate the web.<ref>{{Cite journal|last=OSTERBUR|first=MEGAN|last2=KIEL|first2=CHRISTINA|date=2016-05-02|title=A hegemon fighting for equal rights: the dominant role of COC Nederland in the LGBT transnational advocacy network|journal=Global Networks|language=en|volume=17|issue=2|pages=234–254|doi=10.1111/glob.12126|issn=1470-2266}}</ref> The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.<ref>Osterbur, Megan E. and Christina Kiel. "Pink Links: Visualizing the Global LGBTQ Network" in ''LGBTQ Politics: A Critical Reader''. eds. Marla Brettschneider, Susan Burgess, Christine Keating. pg493-522</ref> 第313行：
第235行： + − ==== Social Media Internet Applications ==== − + 第325行：
第247行： − + − One of the most current methods of the application of SNA is to the study of [[computer-supported collaborative learning]] (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication.<ref name=":0">{{Cite journal|last=Laat|first=Maarten de|last2=Lally|first2=Vic|last3=Lipponen|first3=Lasse|last4=Simons|first4=Robert-Jan|date=2007-03-08|title=Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis|journal=International Journal of Computer-Supported Collaborative Learning|language=en|volume=2|issue=1|pages=87–103|doi=10.1007/s11412-007-9006-4}}</ref> Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network.<ref name=":0" /> When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own. − One of the most current methods of the application of SNA is to the study of computer-supported collaborative learning (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication. Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network. When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own. − 应用国民经济核算体系的最新方法之一是研究电脑支援协作学习。当应用于 CSCL 时，SNA 被用于帮助理解学习者如何在数量、频率和长度方面进行合作，以及交流的质量、主题和策略。此外，系统网络体系结构可以侧重于网络连接的特定方面，或整个网络。它使用图形表示、书面表示和数据表示来帮助检查 CSCL 网络中的连接。当将 SNA 应用于 CSCL 环境时，参与者之间的交互被视为一个社会网络。分析的重点是参与者之间的“联系”——他们如何互动和交流——而不是每个参与者自己的行为。+ + + − ====Key terms====+ − − There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: '''density''', '''centrality''', '''indegree''', '''outdegree''', and '''sociogram'''. − − There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: density, centrality, indegree, outdegree, and sociogram. − − 电脑支援协作学习的社会网络分析研究涉及到几个关键术语，如密度、中心性、独立性、度和社会描述。 − − − − * '''Density''' refers to the "connections" between participants. Density is defined as the number of connections a participant has, divided by the total possible connections a participant could have. For example, if there are 20 people participating, each person could potentially connect to 19 other people. A density of 100% (19/19) is the greatest density in the system. A density of 5% indicates there is only 1 of 19 possible connections.<ref name=":0" /> − − * '''Centrality''' focuses on the behavior of individual participants within a network. It measures the extent to which an individual interacts with other individuals in the network. The more an individual connects to others in a network, the greater their centrality in the network.<ref name=":0" /><ref name=":1" /> − − − − In-degree and out-degree variables are related to centrality. − − In-degree and out-degree variables are related to centrality. − − 度内变量和度外变量与中心性有关。 − − − − * '''In-degree''' centrality concentrates on a specific individual as the point of focus; centrality of all other individuals is based on their relation to the focal point of the "in-degree" individual.<ref name=":0" /> − − * '''Out-degree''' is a measure of centrality that still focuses on a single individual, but the analytic is concerned with the out-going interactions of the individual; the measure of out-degree centrality is how many times the focus point individual interacts with others.<ref name=":0" /><ref name=":1" /> − − * A '''sociogram''' is a visualization with defined boundaries of connections in the network. For example, a sociogram which shows out-degree centrality points for Participant A would illustrate all outgoing connections Participant A made in the studied network.<ref name=":0" /> − − − − ====Unique capabilities==== − − Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers. This particular method allows the study of interaction patterns within a [[Networked learning|networked learning community]] and can help illustrate the extent of the participants' interactions with the other members of the group.<ref name=":0" /> The graphics created using SNA tools provide visualizations of the connections among participants and the strategies used to communicate within the group. Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.<ref>{{cite conference|author1=Palonen, T.|author2= Hakkarainen, K. B.|lastauthoramp=y|editor1=Fishman|editor2=S. O'Connor-Divelbiss|title=Patterns of Interaction in Computer-Supported Learning: A Social Network Analysis|conference=Fourth International Conference of the Learning Sciences|location=Mahwah, NJ|publisher=Erlbaum|pp=334–339|url=http://www.umich.edu/~icls/proceedings/pdf/Palonen.pdf}}</ref> − − Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers. This particular method allows the study of interaction patterns within a networked learning community and can help illustrate the extent of the participants' interactions with the other members of the group. − − 研究人员将社交网络分析应用于电脑支援协作学习的研究，部分原因在于它提供的独特功能。这种特殊的方法允许研究网络化学习社区中的交互模式，并且可以帮助说明参与者与小组其他成员的交互程度。 − − − − A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence,<ref name=":0" /> a greater regard for the recommendations of "central" participants,<ref>{{Cite journal|last=Martı́nez|first=A.|last2=Dimitriadis|first2=Y.|last3=Rubia|first3=B.|last4=Gómez|first4=E.|last5=de la Fuente|first5=P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education|series=Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474}}</ref> infrequency of cross-gender interaction in a network,<ref>{{cite conference|author1=Cho, H.|author2=Stefanone, M.|author3=Gay, G|lastauthoramp=y|year=2002|title=Social information sharing in a CSCL community|conference=Computer support for collaborative learning: Foundations for a CSCL community|location=Hillsdale, NJ|publisher=Lawrence Erlbaum|pages=43–50|citeseerx=10.1.1.225.5273}}</ref> and the relatively small role played by an instructor in an [[asynchronous learning]] network.<ref>{{cite journal|author1=Aviv, R.|author2=Erlich, Z.|author3=Ravid, G.|author4=Geva, A.|lastauthoramp=y|year=2003|title=Network analysis of knowledge construction in asynchronous learning networks|journal=Journal of Asynchronous Learning Networks|volume=7|issue=3|pp=1–23|citeseerx=10.1.1.2.9044}}</ref> 第383行：
第263行： + − ====Other methods used alongside SNA==== − Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,<ref name=":0" /> researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL. The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL.<ref>{{Cite book|title=Groupware: Design, Implementation, and Use|last=Daradoumis|first=Thanasis|last2=Martínez-Monés|first2=Alejandra|last3=Xhafa|first3=Fatos|date=2004-09-05|publisher=Springer Berlin Heidelberg|isbn=9783540230168|editor-last=Vreede|editor-first=Gert-Jan de|series=Lecture Notes in Computer Science|pages=[https://archive.org/details/unset0000unse_i0a6/page/289 289–304]|language=en|doi=10.1007/978-3-540-30112-7_25|editor-last2=Guerrero|editor-first2=Luis A.|editor-last3=Raventós|editor-first3=Gabriela Marín|hdl=2117/116654|url-access=registration|url=https://archive.org/details/unset0000unse_i0a6/page/289}}</ref> Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.<ref name=autogenerated1/>+ 第393行：
第273行： + + − A number of research studies have combined other types of analysis with SNA in the study of CSCL. This can be referred to as a multi-method approach or data [[Triangulation (social science)|triangulation]], which will lead to an increase of evaluation [[Reliability (statistics)|reliability]] in CSCL studies. − A number of research studies have combined other types of analysis with SNA in the study of CSCL. This can be referred to as a multi-method approach or data triangulation, which will lead to an increase of evaluation reliability in CSCL studies. − 在 CSCL 的研究中，许多研究将其他类型的分析与 SNA 相结合。这可以被称为多方法方法或数据三角测量，这将导致 CSCL 研究的评价可靠性的增加。+ + + − * Qualitative method – The principles of qualitative case study research constitute a solid framework for the integration of SNA methods in the study of CSCL experiences.<ref>{{Cite journal|last=Johnson|first=Karen E.|date=1996-01-01|title=Review of The Art of Case Study Research|jstor=329758|journal=The Modern Language Journal|volume=80|issue=4|pages=556–557|doi=10.2307/329758}}</ref>+ − **''[[Ethnography|Ethnographic]] data'' such as student questionnaires and interviews and classroom non-participant observations<ref name=autogenerated1>{{cite journal|author1=Martı́nez, A.|author2=Dimitriadis, Y.|author3=Rubia, B.|author4=Gómez, E.|author5=de la Fuente, P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education. Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474}}</ref> − **''[[Case study|Case studies]]'': comprehensively study particular CSCL situations and relate findings to general schemes<ref name=autogenerated1 /> − **''[[Content analysis]]:'' offers information about the content of the communication among members<ref name=autogenerated1 />+ − − * Quantitative method – This includes simple descriptive statistical analyses on occurrences to identify particular attitudes of group members who have not been able to be tracked via SNA in order to detect general tendencies. − **''Computer [[Logfile|log files]]:'' provide automatic data on how collaborative tools are used by learners<ref name=autogenerated1 />+ − **''[[Multidimensional scaling|Multidimensional scaling (MDS)]]'': charts similarities among actors, so that more similar input data is closer together<ref name=autogenerated1 /> − **''[[Software]] tools:'' QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST<ref name=autogenerated1 /> − + − − − − {{div col|colwidth=22em}} − − * [[Actor-network theory]] − − * [[Community structure]] − − * [[Complex network]] − − * [[Digital humanities]] − − * [[Dynamic network analysis]] − − * [[Friendship paradox]] − − * [[Individual mobility]] − − * [[Mathematical sociology]] − − * [[Metcalfe's law]] − − * [[Network-based diffusion analysis]] − − * [[Network science]] − − * [[Organizational patterns]] − − * [[Small world phenomenon]] − − * [[Social media analytics]] − − * [[Social media mining]] − − * [[Social network]] − − * [[Social network analysis software]] − − * [[Social networking service]] − − * [[Social software]] − − * [[Social web]] − − * [[Sociomapping]] − − * [[Attention inequality]] − − {{div col end}} − − − − ==References== − − {{reflist|colwidth=35em|refs= − − {{reflist|colwidth=35em|refs= − − {通货再膨胀 | colwidth 35em | refs − − <ref name="asanet">{{cite journal|author1=Moody, James|author2=Douglas R. White|lastauthoramp=y|year=2003|title=Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups|journal=American Sociological Review|volume=68|issue=1|pages=103–127|url=http://eclectic.ss.uci.edu/~drwhite/soc_con17.pdf|citeseerx=10.1.1.18.5695|doi=10.2307/3088904|jstor=3088904}}</ref> − − <ref name="comprehensive">The most comprehensive reference is: {{cite book|author1=Wasserman, Stanley|author2=Faust, Katherine|lastauthoramp=y|year=1994|title=Social Networks Analysis: Methods and Applications|location=Cambridge|publisher=Cambridge University Press|url=https://books.google.com/books?isbn=0521387078}} A short, clear basic summary is in {{cite journal|author1=Krebs, Valdis|authorlink=Valdis Krebs|year=2000|title=The Social Life of Routers|journal=Internet Protocol Journal|volume=3 (December)|pages=14–25}}</ref> − − <ref name="development">{{cite book|author=Linton Freeman|title=The Development of Social Network Analysis|location=Vancouver|publisher=Empirical Press|year=2006}}</ref> − − <ref name="interpreting">{{cite journal|last1=McGrath|last2=Blythe|last3=Krackhardt|lastauthoramp=y|year=1997|title=The effect of spatial arrangement on judgements and errors in interpreting graphs|journal=Social Networks|volume=19|issue=3|pages=223–242|doi=10.1016/S0378-8733(96)00299-7|url=http://www.analytictech.com/borgatti/mcgrath.pdf|citeseerx=10.1.1.121.5856}}</ref> − − <ref name="uci">[http://intersci.ss.uci.edu/wiki/index.php/Cohesive_blocking Cohesive.blocking] is the R program for computing structural cohesion according to the Moody-White (2003) algorithm. This wiki site provides numerous examples and a tutorial for use with R.</ref> − − <ref name="visualizing">{{cite journal|author1=Bernie Hogan|author2=Juan-Antonio Carrasco |author3= Barry Wellman|lastauthoramp=y|title=Visualizing Personal Networks: Working with Participant-Aided Sociograms|journal=Field Methods|volume=19|issue=2|date=May 2007|pages=116–144|doi=10.1177/1525822X06298589|url=http://groups.chass.utoronto.ca/netlab/wp-content/uploads/2012/05/Visualizing-Personal-Networks-Working-with-Participant-Aided-Sociograms.pdf}}</ref> − − <ref name="Kadu12">{{cite book|author=Kadushin, C.|year=2012|title=Understanding social networks: Theories, concepts, and findings|location= Oxford|publisher=Oxford University Press|url=https://books.google.com/books?id=ALOhpMgkW_cC&printsec=frontcover#v=onepage&q=mutuality%20OR%20reciprocity&f=false|isbn=9780195379471}}</ref> − − <ref name="Podo97">{{cite journal|author1=Podolny, J.M.|author2=Baron, J.N.|lastauthoramp=y|year=1997|title=Resources and relationships: Social networks and mobility in the workplace|journal=American Sociological Review|volume=62|issue=5|pages=673–693|jstor=2657354|doi=10.2307/2657354|citeseerx=10.1.1.114.6822}}</ref> − − <ref name="Flyn10">{{cite journal | last1 = Flynn | first1 = F.J. | last2 = Reagans | first2 = R.E. | last3 = Guillory | first3 = L. | year = 2010 | title = Do you two know each other? Transitivity, homophily, and the need for (network) closure | url = https://semanticscholar.org/paper/7e46638f3ff33f2a80fd2f3dd5efd246e7ac5a8f| journal = Journal of Personality and Social Psychology | volume = 99 | issue = 5| pages = 855–869 | doi=10.1037/a0020961 | pmid=20954787}}</ref> − − <ref name="Golbeck">{{cite book|author1=Golbeck, J.|year=2013|title=Analyzing the Social Web|publisher= Morgan Kaufmann|isbn=978-0-12-405856-9}}</ref> 第505行：
第301行： − + − + − + − + − + − ! -- 本页面仅供社交网络使用。请只在这些文章上张贴社交网站和社交网络服务的链接。--> − <!--======================== {{No more links}} ============================ 第525行：
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第387行： − + − * [https://github.com/briatte/awesome-network-analysis/ Awesome Network Analysis] (200+ links to books, conferences, courses, journals, research groups, software, tutorials and more)+ − * [https://web.archive.org/web/20110516133549/http://stat.gamma.rug.nl/SnijdersSteglichVdBunt2009.pdf Introduction to Stochastic Actor-Based Models for Network Dynamics - Snijders et al.] − * [http://www.casos.cs.cmu.edu Center for Computational Analysis of Social and Organizational Systems (CASOS) at Carnegie Mellon] − * [http://www.chass.utoronto.ca/~wellman/netlab/ABOUT/index.html NetLab at the University of Toronto, studies the intersection of social, communication, information and computing networks]+ − * [http://netwiki.amath.unc.edu/ Netwiki] (wiki page devoted to social networks; maintained at University of North Carolina at Chapel Hill) − * [https://web.archive.org/web/20080215084223/http://www.ksg.harvard.edu/netgov/ Program on Networked Governance] – Program on Networked Governance, Harvard University − * [http://www.snakdd.com The International Workshop on Social Network Analysis and Mining] (SNA-KDD) - An annual workshop on social network analysis and mining, with participants from computer science, social science, and related disciplines.+ − * [https://web.archive.org/web/20110217151303/http://www.oeaw.ac.at/byzanz/historicaldynamics.htm Historical Dynamics in a time of Crisis: Late Byzantium, 1204–1453 (a discussion of social network analysis from the point of view of historical studies)]+ − * [https://leb.fbi.gov/2013/march/social-network-analysis-a-systematic-approach-for-investigating Social Network Analysis: A Systematic Approach for Investigating] + − ===Organizations=== − * [http://www.insna.org/ International Network for Social Network Analysis] + + − + − + − + − + − + − + − * ''[https://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278 Social Network Analysis and Mining (SNAM)]'' − * {{cite journal |title=REDES |location=Spain |publisher=Universidad Autónoma de Barcelona y Universidad de Sevilla |url=http://revista-redes.rediris.es/ }} − * {{cite journal |title=Connections |publisher=International Network for Social Network Analysis |url=http://www.insna.org/pubs/connections/ |url-status=dead |archiveurl=https://web.archive.org/web/20130718063641/http://www.insna.org/pubs/connections/ |archivedate=2013-07-18 }}+ + + − ===Textbooks and educational resources===+ − + − + − + − + + + − {{Commons category|Social networks}}+ + + − {{Social networking}}+ + + − {{DEFAULTSORT:Social Network}}+ − − 第669行：
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Moved page from wikipedia:en:Social network analysis (history)
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{{about|the theoretical concept|social networking sites|social networking service|other uses|Social network (disambiguation)}}
{{about|the theoretical concept|quantitative application to social media|social media analytics|social networking sites|social networking service|other uses|Social network (disambiguation)}}
{{short description|Analysis of social structures using network and graph theory}}
{{short description|Analysis of social structures using network and graph theory}}
显示一组 Facebook 用户之间友谊关系的社交网络图。]
显示一组 Facebook 用户之间友谊关系的社交网络图。]
'''Social network analysis''' ('''SNA''') is the process of investigating social structures through the use of [[Network theory|networks]] and [[graph theory]].<ref>{{Cite journal|title = Social network analysis: a powerful strategy, also for the information sciences|last = Otte|first = Evelien|date = 2002|journal = Journal of Information Science|doi = 10.1177/016555150202800601|pmid = |last2 = Rousseau|first2 = Ronald|volume=28|issue = 6|pages=441–453}}</ref> It characterizes networked structures in terms of ''nodes'' (individual actors, people, or things within the network) and the ''ties'', ''edges'', or ''links'' (relationships or interactions) that connect them. Examples of [[social structure]]s commonly visualized through social network analysis include [[Social media|social media networks]],<ref>{{Cite journal | volume = 3| issue = 1| last = Grandjean| first = Martin| title = A social network analysis of Twitter: Mapping the digital humanities community| journal =Cogent Arts & Humanities| date = 2016| pages = 1171458|doi=10.1080/23311983.2016.1171458| url = https://serval.unil.ch/resource/serval:BIB_81C2C68B1DF5.P001/REF}}</ref><ref name="Hagen L 2018 523–541">{{Cite journal|last=Hagen L|last2=Neely S|last3=Robert-Cooperman C|last4=Keller T|last5=DePaula N|date=2018|title=Crisis Communications in the Age of Social Media: A Network Analysis of Zika-Related Tweets|journal=Soc. Sci. Comput. Rev. Social Science Computer Review|language=English|volume=36|issue=5|pages=523–541|issn=0894-4393|oclc=7323548177|doi=10.1177/0894439317721985}}</ref> [[Internet meme|memes]] spread,<ref>{{Cite arxiv|last=Nasrinpour|first=Hamid Reza|last2=Friesen|first2=Marcia R.|last3=McLeod|first3=Robert D.|date=2016-11-22|title=An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network|eprint=1611.07454|class=cs.SI}}</ref> information circulation,<ref>{{Cite journal | volume = | issue = 2| last = Grandjean| first = Martin| title = Complex structures and international organizations| trans-title = Analisi e visualizzazioni delle reti in storia. L'esempio della cooperazione intellettuale della Società delle Nazioni | journal = Memoria e Ricerca | date = 2017| pages = 371–393| doi = 10.14647/87204}} See also: [https://halshs.archives-ouvertes.fr/halshs-01610098v2 French version] (PDF) and [http://www.martingrandjean.ch/complex-structures-and-international-organizations/ English summary].</ref> [[Weak ties|friendship and acquaintance networks]], business networks, knowledge networks,<ref name=Brennecke2017>{{Cite journal|last=Brennecke|first=Julia|last2=Rank|first2=Olaf|date=2017-05-01|title=The firm's knowledge network and the transfer of advice among corporate inventors—A multilevel network study|journal=Research Policy|volume=46|issue=4|pages=768–783|doi=10.1016/j.respol.2017.02.002|issn=0048-7333}}</ref><ref name=Harris2009>{{Cite journal|last=Harris|first=Jenine K|last2=Luke|first2=Douglas A|last3=Shelton|first3=Sarah C|last4=Zuckerman|first4=Rachael B|date=2009|title=Forty Years of Secondhand Smoke Research. The Gap Between Discovery and Delivery|doi=10.1016/j.amepre.2009.01.039|pmid=19372026|journal=American Journal of Preventive Medicine|volume=36|issue=6|pages=538–548|issn=0749-3797|oclc=6980180781}}</ref> difficult working relationships,<ref name=Brennecke2019/> social networks, [[collaboration graph]]s, [[kinship]], [[disease transmission]], and [[sexual network|sexual relationships]].<ref>{{cite book|author=Pinheiro, Carlos A.R.|title=Social Network Analysis in Telecommunications|publisher=John Wiley & Sons|year=2011|isbn=978-1-118-01094-5|page=4|url=https://books.google.com/books?id=jP8zfL6yNGkC&pg=PA4}}</ref><ref>{{cite book|author=D'Andrea, Alessia|chapter=An Overview of Methods for Virtual Social Network Analysis|editor1=Abraham, Ajith|title=Computational Social Network Analysis: Trends, Tools and Research Advances|publisher=Springer|year=2009|isbn=978-1-84882-228-3|page=8|chapter-url=https://books.google.com/books?id=-S1KiURSfRAC&pg=PA8|display-authors=etal}}</ref> These networks are often visualized through ''[[sociogram]]s'' in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.<ref>{{Cite journal|last=Grunspan|first=Daniel|date=January 23, 2014|title=Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research|journal=CBE: Life Sciences Education|volume=13|issue=2|pages=167–178|doi=10.1187/cbe.13-08-0162|pmid=26086650|pmc=4041496}}</ref>
'''Social network analysis''' ('''SNA''') is the process of investigating social structures through the use of [[Network theory|networks]] and [[graph theory]].<ref>{{Cite journal|title = Social network analysis: a powerful strategy, also for the information sciences|last1 = Otte|first1 = Evelien|date = 2002|journal = Journal of Information Science|doi = 10.1177/016555150202800601|pmid = |last2 = Rousseau|first2 = Ronald|volume=28|issue = 6|pages=441–453|s2cid = 17454166}}</ref> It characterizes networked structures in terms of ''nodes'' (individual actors, people, or things within the network) and the ''ties'', ''edges'', or ''links'' (relationships or interactions) that connect them. Examples of [[social structure]]s commonly visualized through social network analysis include [[Social media|social media networks]],<ref>{{Cite journal | volume = 3| issue = 1| last = Grandjean| first = Martin| title = A social network analysis of Twitter: Mapping the digital humanities community| journal =Cogent Arts & Humanities| date = 2016| pages = 1171458|doi=10.1080/23311983.2016.1171458| url = https://serval.unil.ch/resource/serval:BIB_81C2C68B1DF5.P001/REF| doi-access = free}}</ref><ref name="Hagen L 2018 523–541">{{Cite journal|last1=Hagen L|last2=Neely S|last3=Robert-Cooperman C|last4=Keller T|last5=DePaula N|date=2018|title=Crisis Communications in the Age of Social Media: A Network Analysis of Zika-Related Tweets|journal=Soc. Sci. Comput. Rev. Social Science Computer Review|language=English|volume=36|issue=5|pages=523–541|issn=0894-4393|oclc=7323548177|doi=10.1177/0894439317721985|s2cid=67362137}}</ref> [[Internet meme|memes]] spread,<ref>{{Cite arxiv|last1=Nasrinpour|first1=Hamid Reza|last2=Friesen|first2=Marcia R.|last3=McLeod|first3=Robert D.|date=2016-11-22|title=An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network|eprint=1611.07454|class=cs.SI}}</ref> information circulation,<ref>{{Cite journal | volume = | issue = 2| last = Grandjean| first = Martin| title = Complex structures and international organizations| trans-title = Analisi e visualizzazioni delle reti in storia. L'esempio della cooperazione intellettuale della Società delle Nazioni | journal = Memoria e Ricerca | date = 2017| pages = 371–393| doi = 10.14647/87204}} See also: [https://halshs.archives-ouvertes.fr/halshs-01610098v2 French version] (PDF) and [http://www.martingrandjean.ch/complex-structures-and-international-organizations/ English summary].</ref> [[Weak ties|friendship and acquaintance networks]], business networks, knowledge networks,<ref name=Brennecke2017>{{Cite journal|last1=Brennecke|first1=Julia|last2=Rank|first2=Olaf|date=2017-05-01|title=The firm's knowledge network and the transfer of advice among corporate inventors—A multilevel network study|journal=Research Policy|volume=46|issue=4|pages=768–783|doi=10.1016/j.respol.2017.02.002|issn=0048-7333}}</ref><ref name=Harris2009>{{Cite journal|last1=Harris|first1=Jenine K|last2=Luke|first2=Douglas A|last3=Shelton|first3=Sarah C|last4=Zuckerman|first4=Rachael B|date=2009|title=Forty Years of Secondhand Smoke Research. The Gap Between Discovery and Delivery|doi=10.1016/j.amepre.2009.01.039|pmid=19372026|journal=American Journal of Preventive Medicine|volume=36|issue=6|pages=538–548|issn=0749-3797|oclc=6980180781}}</ref> difficult working relationships,<ref name=Brennecke2019/> social networks, [[collaboration graph]]s, [[kinship]], [[disease transmission]], and [[sexual network|sexual relationships]].<ref>{{cite book|author=Pinheiro, Carlos A.R.|title=Social Network Analysis in Telecommunications|publisher=John Wiley & Sons|year=2011|isbn=978-1-118-01094-5|page=4|url=https://books.google.com/books?id=jP8zfL6yNGkC&pg=PA4}}</ref><ref>{{cite book|author=D'Andrea, Alessia|chapter=An Overview of Methods for Virtual Social Network Analysis|editor1=Abraham, Ajith|title=Computational Social Network Analysis: Trends, Tools and Research Advances|publisher=Springer|year=2009|isbn=978-1-84882-228-3|page=8|chapter-url=https://books.google.com/books?id=-S1KiURSfRAC&pg=PA8|display-authors=etal}}</ref> These networks are often visualized through ''[[sociogram]]s'' in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.<ref>{{Cite journal|last=Grunspan|first=Daniel|date=January 23, 2014|title=Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research|journal=CBE: Life Sciences Education|volume=13|issue=2|pages=167–178|doi=10.1187/cbe.13-08-0162|pmid=26086650|pmc=4041496}}</ref>
Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, memes spread, information circulation, friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships, These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.
Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, memes spread, information circulation, friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships, These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.
社会网络分析是利用网络和图论研究社会结构的过程。它以节点(个体参与者、人或网络中的事物)和连接它们的关系、边缘或链接(关系或交互)来描述网络结构。通过社会网络分析通常可视化的社会结构的例子包括社会媒体网络，模因传播，信息流通，友谊和熟人网络，商业网络，知识网络，困难的工作关系，这些网络通常是可视化的通过社会图，其中节点表示为点和关系表示为线。这些可视化提供了一种定性评估网络的方法，通过改变它们的节点和边的视觉表示来反映感兴趣的属性。
社会网络分析是利用网络和图论研究社会结构的过程。它以节点(个体参与者、人或网络中的事物)和连接它们的关系、边缘或链接(关系或交互)来描述网络结构。通过社会网络分析通常可视化的社会结构的例子包括社会媒体网络，模因传播，信息流通，友谊和熟人网络，商业网络，知识网络，困难的工作关系，这些网络通常是可视化的通过社会图，其中节点表示为点和关系表示为线。这些可视化通过改变网络节点和边的视觉表示来反映感兴趣的属性，从而提供了一种定性评估网络的方法。
Social network analysis has emerged as a key technique in modern [[sociology]]. It has also gained a significant following in [[anthropology]], [[biology]],<ref>{{Cite journal|last=Tringali|first=Angela|last2=Sherer|first2=David L.|last3=Cosgrove|first3=Jillian|last4=Bowman|first4=Reed|date=2020-02-10|title=Life history stage explains behavior in a social network before and during the early breeding season in a cooperatively breeding bird|journal=PeerJ|language=en|volume=8|pages=e8302|doi=10.7717/peerj.8302|pmid=32095315|pmc=7020825|issn=2167-8359}}</ref> [[demography]], [[communication studies]],<ref name="Hagen L 2018 523–541"/><ref name=":1">{{Cite book|title=Social network differences of chronotypes identified from mobile phone data|date=2018|language=English|oclc=1062367169}}</ref> [[economics]], [[geography]], [[history]], [[information science]], [[organizational studies]],<ref name=Brennecke2017/><ref name=Brennecke2019/> [[political science]], public health,<ref name=Harris2007>{{Cite journal|last1=Harris|first1=J.K|last2=Clements|first2=B|date=2007|title=Using social network analysis to understand Missouri's system of public health emergency planners|doi=10.1177/003335490712200410|pmid=17639652|pmc=1888499|journal=Public Health Rep. Public Health Reports|language=English|volume=122|issue=4|pages=488–498|issn=0033-3549|oclc=8062393936}}</ref><ref name=Harris2009/> [[social psychology]], [[development studies]], [[sociolinguistics]], and [[computer science]]<ref name=Ghanbarnejad>{{Cite book|title=Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches|last=Ghanbarnejad|first=Fakhteh|last2=Saha Roy|first2=Rishiraj|last3=Karimi|first3=Fariba|last4=Delvenne|first4=Jean-Charles|last5=Mitra|first5=Bivas|date=2019|publisher=Springer International Publishing : Imprint: Springer|isbn=9783030146832|location=Cham|language=English|oclc=1115074203}}</ref> and is now commonly available as a consumer tool (see the [[Social network analysis software|list of SNA software]]).<ref>{{cite web|url=https://www.bbc.co.uk/news/technology-19699776|title=Facebook friends mapped by Wolfram Alpha app|work=BBC News|date=September 24, 2012|accessdate=July 25, 2016}}</ref><ref>{{cite web|url=https://techcrunch.com/2012/08/30/wolfram-alpha-launches-personal-analytics-reports-for-facebook/|title=Wolfram Alpha Launches Personal Analytics Reports For Facebook|work=Tech Crunch|date= August 30, 2012|author=Frederic Lardinois|accessdate=July 25, 2016}}</ref><ref>[http://www.irh.org/?q=content/terikunda-j%C3%A9kulu-project Institute of Reproductive Health]</ref><ref>{{cite journal|author1=Ivaldi M.|author2=Ferreri L.|author3=Daolio F.|author4=Giacobini M.|author5=Tomassini M.|author6=Rainoldi A.|title=We-Sport: from academy spin-off to data-base for complex network analysis; an innovative approach to a new technology|journal=J Sports Med and Phys Fitnes |volume=51|issue=suppl. 1 to issue 3|url=https://iris.unito.it/handle/2318/90491}} The social network analysis was used to analyze properties of the network We-Sport.com allowing a deep interpretation and analysis of the level of aggregation phenomena in the specific context of sport and physical exercise.</ref>
Social network analysis has emerged as a key technique in modern [[sociology]]. It has also gained a significant following in [[anthropology]], [[biology]],<ref>{{Cite journal|last1=Tringali|first1=Angela|last2=Sherer|first2=David L.|last3=Cosgrove|first3=Jillian|last4=Bowman|first4=Reed|date=2020-02-10|title=Life history stage explains behavior in a social network before and during the early breeding season in a cooperatively breeding bird|journal=PeerJ|language=en|volume=8|pages=e8302|doi=10.7717/peerj.8302|pmid=32095315|pmc=7020825|issn=2167-8359}}</ref> [[demography]], [[communication studies]],<ref name="Hagen L 2018 523–541"/><ref name=":1">{{Cite book|title=Social network differences of chronotypes identified from mobile phone data|date=2018|language=English|oclc=1062367169}}</ref> [[economics]], [[geography]], [[history]], [[information science]], [[organizational studies]],<ref name=Brennecke2017/><ref name=Brennecke2019/> [[political science]], public health,<ref name=Harris2007>{{Cite journal|last1=Harris|first1=J.K|last2=Clements|first2=B|date=2007|title=Using social network analysis to understand Missouri's system of public health emergency planners|doi=10.1177/003335490712200410|pmid=17639652|pmc=1888499|journal=Public Health Rep. Public Health Reports|language=English|volume=122|issue=4|pages=488–498|issn=0033-3549|oclc=8062393936}}</ref><ref name=Harris2009/> [[social psychology]], [[development studies]], [[sociolinguistics]], and [[computer science]]<ref name=Ghanbarnejad>{{Cite book|title=Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches|last1=Ghanbarnejad|first1=Fakhteh|last2=Saha Roy|first2=Rishiraj|last3=Karimi|first3=Fariba|last4=Delvenne|first4=Jean-Charles|last5=Mitra|first5=Bivas|date=2019|publisher=Springer International Publishing : Imprint: Springer|isbn=9783030146832|location=Cham|language=English|oclc=1115074203}}</ref> and is now commonly available as a consumer tool (see the [[Social network analysis software|list of SNA software]]).<ref>{{cite web|url=https://www.bbc.co.uk/news/technology-19699776|title=Facebook friends mapped by Wolfram Alpha app|work=BBC News|date=September 24, 2012|accessdate=July 25, 2016}}</ref><ref>{{cite web|url=https://techcrunch.com/2012/08/30/wolfram-alpha-launches-personal-analytics-reports-for-facebook/|title=Wolfram Alpha Launches Personal Analytics Reports For Facebook|work=Tech Crunch|date= August 30, 2012|author=Frederic Lardinois|accessdate=July 25, 2016}}</ref><ref>[http://www.irh.org/?q=content/terikunda-j%C3%A9kulu-project Institute of Reproductive Health]</ref><ref>{{cite journal|author1=Ivaldi M.|author2=Ferreri L.|author3=Daolio F.|author4=Giacobini M.|author5=Tomassini M.|author6=Rainoldi A.|title=We-Sport: from academy spin-off to data-base for complex network analysis; an innovative approach to a new technology|journal=J Sports Med and Phys Fitnes |volume=51|issue=suppl. 1 to issue 3|url=https://iris.unito.it/handle/2318/90491}} The social network analysis was used to analyze properties of the network We-Sport.com allowing a deep interpretation and analysis of the level of aggregation phenomena in the specific context of sport and physical exercise.</ref>
Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, and is now commonly available as a consumer tool (see the list of SNA software).
Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, and is now commonly available as a consumer tool (see the list of SNA software).
==History==
==History==
Social network analysis has its theoretical roots in the work of early sociologists such as [[Georg Simmel]] and [[Émile Durkheim]], who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "[[social networks]]" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s [[Jacob Moreno]] and [[Helen Hall Jennings|Helen Jennings]] introduced basic analytical methods.<ref>{{cite book |author=Freeman, L. C. |year=2004 |title= The development of social network analysis: a study in the sociology of science |location=Vancouver, B. C. |publisher=Empirical Press|url=https://www.researchgate.net/publication/239228599}}</ref> In 1954, [[John Arundel Barnes]] started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded [[Group (sociology)|groups]] (e.g., tribes, families) and social [[Categorization|categories]] (e.g., gender, ethnicity). Scholars such as [[Ronald Burt]], [[Kathleen Carley]], [[Mark Granovetter]], [[David Krackhardt]], [[Edward Laumann]], [[Anatol Rapoport#Social network analysis|Anatol Rapoport]], [[Barry Wellman]], [[Douglas R. White]], and [[Harrison White]] expanded the use of systematic social network analysis.<ref name="development"/> Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H.K. | last2 = Gerhards | first2 = J. | last3 = Romo | first3 = F.P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = https://semanticscholar.org/paper/51f01c42ab51d6bdab2a3b6a0ed9b1824c91b746| journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603}}</ref> Wouter 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 = 305–27 | doi=10.1016/s0304-422x(03)00035-4}}</ref> and Burgert Senekal.<ref>Senekal, B. A. 2012. Die Afrikaanse literêre sisteem: ʼn Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA), LitNet Akademies 9(3)</ref> Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering [[money laundering]] and [[terrorism]].
Social network analysis has its theoretical roots in the work of early sociologists such as [[Georg Simmel]] and [[Émile Durkheim]], who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "[[social networks]]" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s [[Jacob Moreno]] and [[Helen Hall Jennings|Helen Jennings]] introduced basic analytical methods.<ref>{{cite book |author=Freeman, L. C. |year=2004 |title= The development of social network analysis: a study in the sociology of science |location=Vancouver, B. C. |publisher=Empirical Press|url=https://www.researchgate.net/publication/239228599}}</ref> In 1954, [[John Arundel Barnes]] started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded [[Group (sociology)|groups]] (e.g., tribes, families) and social [[Categorization|categories]] (e.g., gender, ethnicity). Scholars such as [[Ronald Burt]], [[Kathleen Carley]], [[Mark Granovetter]], [[David Krackhardt]], [[Edward Laumann]], [[Anatol Rapoport#Social network analysis|Anatol Rapoport]], [[Barry Wellman]], [[Douglas R. White]], and [[Harrison White]] expanded the use of systematic social network analysis.<ref name="development"/> Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,<ref>{{cite journal | last1 = Anheier | first1 = H.K. | last2 = Gerhards | first2 = J. | last3 = Romo | first3 = F.P. | year = 1995 | title = Forms of capital and social structure of fields: examining Bourdieu's social topography | url = https://semanticscholar.org/paper/51f01c42ab51d6bdab2a3b6a0ed9b1824c91b746| journal = American Journal of Sociology | volume = 100 | issue = 4| pages = 859–903 | doi=10.1086/230603| s2cid = 143587142 }}</ref> Wouter 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 = 305–27 | doi=10.1016/s0304-422x(03)00035-4}}</ref> and Burgert Senekal.<ref>Senekal, B. A. 2012. Die Afrikaanse literêre sisteem: ʼn Eksperimentele benadering met behulp van Sosiale-netwerk-analise (SNA), LitNet Akademies 9(3)</ref> Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering [[money laundering]] and [[terrorism]].
Social network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Émile Durkheim, who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "social networks" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods. In 1954, John Arundel Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Wouter De Nooy, and Burgert Senekal. Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering money laundering and terrorism.
Social network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Émile Durkheim, who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "social networks" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods. In 1954, John Arundel Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Wouter De Nooy, and Burgert Senekal. Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering money laundering and terrorism.
社会网络分析的理论根源在于早期社会学家的工作，如 Georg Simmel 和 mile Durkheim，他们写了关于研究连接社会行为者的关系模式的重要性的文章。自20世纪早期以来，社会科学家一直使用”社会网络”的概念来表示社会系统成员之间各种规模的复杂关系，从人际关系到国际关系。20世纪30年代，雅各布 · 莫雷诺和海伦 · 詹宁斯介绍了基本的分析方法。1954年，约翰 · 阿伦德尔 · 巴恩斯开始系统地使用这个词来表示关系模式，包括传统上公众使用的概念和社会科学家使用的概念: 有界群体(如部落、家庭)和社会分类(如性别、种族)。学者们，如 Ronald Burt，Kathleen Carley，马克·格兰诺维特，David Krackhardt，Edward Laumann，Anatol Rapoport，Barry Wellman，Douglas r. White，and Harrison White 扩展了系统社会网络分析的应用。和 Burgert Senekal。事实上，社交网络分析已经在不同的学术领域得到了应用，还有一些实际的应用，比如打击洗钱组织和恐怖主义。
社会网络分析的理论根源在于早期社会学家的工作，如 Georg Simmel 和 é mile Durkheim，他们写了关于研究连接社会行为者的关系模式的重要性的文章。自20世纪早期以来，社会科学家一直使用”社会网络”的概念来表示社会系统成员之间各种规模的复杂关系，从人际关系到国际关系。20世纪30年代，雅各布 · 莫雷诺和海伦 · 詹宁斯介绍了基本的分析方法。1954年，约翰 · 阿伦德尔 · 巴恩斯开始系统地使用这个词来表示关系模式，包括传统上公众使用的概念和社会科学家使用的概念: 有界的群体(如部落、家庭)和社会类别(如性别、种族)。学者们，如 Ronald Burt，Kathleen Carley，马克·格兰诺维特，David Krackhardt，Edward Laumann，Anatol Rapoport，Barry Wellman，Douglas r. White，and Harrison White 扩展了系统社会网络分析的应用。和 Burgert Senekal。事实上，社交网络分析已经在各种学术领域得到了应用，同时也在打击洗钱组织和恐怖主义等实际应用中得到了应用。
Hue (from red=0 to blue=max) indicates each node's [[betweenness centrality.]]Size: The number of network members in a given network.
Hue (from red=0 to blue=max) indicates each node's [[betweenness centrality.]]Size: The number of network members in a given network.
色调(从红色0到蓝色 max)表示每个节点的[[介于中心性之间]。[英语背诵文选大小: 给定网络中网络成员的数量。
色调(从红色 = 0到蓝色 = max)表示每个节点的[[中间集中性]。[英语背诵文选大小: 给定网络中网络成员的数量。
===Connections===
===Connections===
[[Homophily]]: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic.<ref>{{cite journal |author1=McPherson, N.|author2=Smith-Lovin, L.|author3=Cook, J.M. |year=2001 |title=Birds of a feather: Homophily in social networks |journal=Annual Review of Sociology |volume=27 |pages=415–444 |doi=10.1146/annurev.soc.27.1.415|url=https://semanticscholar.org/paper/312048ca4295d3639dce516f4691c116afda0e5d}}</ref> Homophily is also referred to as [[assortativity]].
[[Homophily]]: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic.<ref>{{cite journal |author1=McPherson, N.|author2=Smith-Lovin, L.|author3=Cook, J.M. |year=2001 |title=Birds of a feather: Homophily in social networks |journal=Annual Review of Sociology |volume=27 |pages=415–444 |doi=10.1146/annurev.soc.27.1.415|s2cid=2341021|url=https://semanticscholar.org/paper/312048ca4295d3639dce516f4691c116afda0e5d}}</ref> Homophily is also referred to as [[assortativity]].
Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic. Homophily is also referred to as assortativity.
Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic. Homophily is also referred to as assortativity.
同质性: 演员与相似的人或不相似的人形成联系的程度。相似性可以通过性别、种族、年龄、职业、教育成就、地位、价值观或任何其他显著特征来定义。同质性也被称为同质性。
同质性: 演员在多大程度上形成相似或不同的关系。相似性可以通过性别、种族、年龄、职业、教育成就、地位、价值观或任何其他显著特征来定义。同质性也被称为同质性。
Multiplexity: The number of content-forms contained in a tie.<ref name = "Podo97"/> For example, two people who are friends and also work together would have a multiplexity of 2.<ref>{{cite book |author1=Kilduff, M.|author2=Tsai, W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref> Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.<ref name=Brennecke2019>{{Cite journal|last=Brennecke|first=Julia|date=2019|title=Dissonant Ties in Intraorganizational Networks: Why Individuals Seek Problem-Solving Assistance from Difficult Colleagues|doi=10.5465/amj.2017.0399|journal=Academy of Management Journal|language=English|issn=0001-4273|oclc=8163488129}}</ref>
Multiplexity: The number of content-forms contained in a tie.<ref name = "Podo97"/> For example, two people who are friends and also work together would have a multiplexity of 2.<ref>{{cite book |author1=Kilduff, M.|author2=Tsai, W. |year=2003 |title= Social networks and organisations |publisher=Sage Publications}}</ref> Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.<ref name=Brennecke2019>{{Cite journal|last=Brennecke|first=Julia|date=2019|title=Dissonant Ties in Intraorganizational Networks: Why Individuals Seek Problem-Solving Assistance from Difficult Colleagues|doi=10.5465/amj.2017.0399|journal=Academy of Management Journal|volume=63|issue=3|pages=743–778|language=English|issn=0001-4273|oclc=8163488129}}</ref>
Multiplexity: The number of content-forms contained in a tie. Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.
Multiplexity: The number of content-forms contained in a tie. Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.
Mutuality/Reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction.
Mutuality/Reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction.
相互性 / 互惠性: 两个演员互惠对方的友谊或其他互动的程度。
相互性/互惠性: 两个演员互惠对方的友谊或其他互动的程度。
[[Triadic closure|Network Closure]]: A measure of the completeness of relational triads. An individual's assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of [[Closure (psychology)|Need for Cognitive Closure]].<ref name="Flyn10"/>
[[Triadic closure|Network Closure]]: A measure of the completeness of relational triads. An individual's assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of [[Closure (psychology)|Need for Cognitive Closure]].<ref name="Flyn10"/>
Centrality: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network. Examples of common methods of measuring "centrality" include betweenness centrality,
中心性: 中心性是指一组度量标准，旨在量化网络中特定节点(或组)的“重要性”或“影响力”(在各种意义上)。测量“中心性”的常用方法包括介于中心性、中心性和中心性之间,
[[Propinquity]]: The tendency for actors to have more ties with geographically close others.<ref name="Kadu12"/>
[[Propinquity]]: The tendency for actors to have more ties with geographically close others.<ref name="Kadu12"/>
Density: The proportion of direct ties in a network relative to the total number possible.
密度: 一个网络中直接联系在可能总数中所占的比例。
===Distributions===
===Distributions===
Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram's small world experiment and the idea of 'six degrees of separation'.
Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram's small world experiment and the idea of 'six degrees of separation'.
距离: 由 Stanley Milgram 的小世界实验和六度分隔理论的想法推广的连接两个特定演员所需要的最小关系数。
距离: 由 Stanley Milgram 的小世界实验和六度分隔理论的想法推广的连接两个特定演员所需要的最小关系数。
[[Bridge (graph theory)|Bridge]]: An individual whose weak ties fill a [[Structural holes|structural hole]], providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.<ref name="Granovetter, M. 1973 1360–1380">{{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|s2cid=59578641 |url=https://semanticscholar.org/paper/c9aece346139711b8c65c618da99cdbecb162575 }}</ref>
Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.
Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.
结构漏洞: 一个网络的两个部分之间没有联系。发现和利用结构性漏洞可以给企业家带来竞争优势。这个概念是由社会学家罗纳德 · 伯特提出的，有时也被称为社会资本的另一个概念。
结构漏洞: 一个网络的两个部分之间没有联系。发现和利用结构性漏洞可以给企业家带来竞争优势。这个概念是由社会学家罗纳德 · 伯特提出的，有时也被称为社会资本的另一个概念。
[[Centrality]]: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network.<ref>{{cite book|author=Hansen, Derek|title=Analyzing Social Media Networks with NodeXL|publisher=Morgan Kaufmann|year=2010|isbn=978-0-12-382229-1|page=32|url=https://books.google.com/books?id=rbxPm93PRY8C&pg=PA32|display-authors=etal}}</ref><ref>{{cite book|author=Liu, Bing|title=Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data|publisher=Springer|year=2011|isbn=978-3-642-19459-7|page=271|url=https://books.google.com/books?id=jnCi0Cq1YVkC&pg=PA271}}</ref><ref>{{cite book|author=Hanneman, Robert A.|author2=Riddle, Mark|name-list-style=amp|chapter=Concepts and Measures for Basic Network Analysis|title=The Sage Handbook of Social Network Analysis|publisher=SAGE|year=2011|isbn=978-1-84787-395-8|pages=364–367|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA364}}</ref><ref>{{cite book|author1=Tsvetovat, Maksim |author2=Kouznetsov, Alexander|name-list-style=amp|title=Social Network Analysis for Startups: Finding Connections on the Social Web|publisher=O'Reilly|year=2011|isbn=978-1-4493-1762-1|page=45|url=https://books.google.com/books?id=hVOxjkoLSiEC&pg=PA45}}</ref> Examples of common methods of measuring "centrality" include [[betweenness centrality]],<ref name="comprehensive"/> [[closeness centrality]], [[eigenvector centrality]], [[alpha centrality]], and [[degree centrality]].<ref>{{cite journal | last1 = Opsahl | first1 = Tore | last2 = Agneessens | first2 = Filip | last3 = Skvoretz | first3 = John | title = Node centrality in weighted networks: Generalizing degree and shortest paths | doi = 10.1016/j.socnet.2010.03.006 | year = 2010 | pages = 245–251 | volume = 32 | journal = Social Networks | url=http://toreopsahl.com/2010/04/21/article-node-centrality-in-weighted-networks-generalizing-degree-and-shortest-paths/ | issue = 3 }}</ref>
Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality). Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.
Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).
联系强度: 由时间线性组合、情感强度、亲密度和相互作用来定义。相互性)。强关系与同质性、亲近性和传递性有关，而弱关系与桥梁有关。
联系强度: 根据时间线性组合、情感强度、亲密度和相互作用来定义。相互性)。
[[Dense graph|Density]]: The proportion of direct ties in a network relative to the total number possible.<ref>{{cite book|chapter=Social Network Analysis|title=Field Manual 3-24: Counterinsurgency|publisher=Headquarters, [[Department of the Army]]|pages=B–11 – B–12|chapter-url=https://fas.org/irp/doddir/army/fm3-24.pdf}}</ref><ref>{{cite book|author=Xu, Guandong |title=Web Mining and Social Networking: Techniques and Applications|publisher=Springer|year=2010|isbn=978-1-4419-7734-2|page=25|url=https://books.google.com/books?id=mXo9zKeYa6cC&pg=PA25|display-authors=etal}}</ref>
[[Clustering coefficient]]: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'.<ref>{{cite book|author=Hanneman, Robert A.|author2=Riddle, Mark|lastauthoramp=y|chapter=Concepts and Measures for Basic Network Analysis|title=The Sage Handbook of Social Network Analysis|publisher=SAGE|year=2011|isbn=978-1-84787-395-8|pages=346–347|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA346}}</ref>
Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.
Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group.
凝聚力: 参与者之间通过有凝聚力的纽带直接联系在一起的程度。结构内聚性是指如果从一个组中删除，将断开该组的最小成员数。
凝聚力: 参与者之间通过有凝聚力的纽带直接联系在一起的程度。结构内聚性是指如果从一个组中删除，将断开该组的最小成员数。
Distance: The minimum number of ties required to connect two particular actors, as popularized by [[Stanley Milgram]]'s [[small world experiment]] and the idea of 'six degrees of separation'.
Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an [[entrepreneur]] a competitive advantage. This concept was developed by sociologist [[Ronald Stuart Burt|Ronald Burt]], and is sometimes referred to as an alternate conception of social capital.
Visual representation of social networks is important to understand the network data and convey the result of the analysis. Numerous methods of visualization for data produced by social network analysis have been presented. Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.
Visual representation of social networks is important to understand the network data and convey the result of the analysis. Numerous methods of visualization for data produced by social network analysis have been presented. Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.
社会网络的可视化表示对于理解网络数据和传达分析结果具有重要意义。社会网络分析产生的数据可视化的许多方法已经被提出。许多分析软件都有网络可视化模块。通过以各种布局显示节点和关系，并将颜色、大小和其他高级属性归属于节点，可以对数据进行探索。网络的可视化表示可能是传递复杂信息的一种强有力的方法，但是在仅仅从可视化显示中解释节点和图形属性时应该注意，因为它们可能通过定量分析更好地表达结构属性。
Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).<ref name="Granovetter, M. 1973 1360–1380"/> Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.
Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * Net-map toolbox. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected. to represent both the size of an individual's social network and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is Alpha User, defined as a person with a high SNP.
特别是在使用社会网络分析作为促进变革的工具时，参与式网络制图的不同方法已被证明是有用的。在这里，参与者/面试者在数据收集会议期间通过实际绘制网络(用笔和纸或数字)来提供网络数据。笔和纸的网络映射方法的一个例子是 * Net-map 工具箱，它还包括一些参与者属性的集合(感知的影响力和参与者的目标)。这种方法的一个好处是，它允许研究人员收集定性数据，并在收集网络数据时提出澄清问题。代表了一个人的社交网络的大小和他们影响这个网络的能力。SNP 系数是由 Bob Gerstley 于2002年首次定义和使用的。一个密切相关的术语是阿尔法用户，定义为高 SNP 的人。
===Segmentation===
SNP coefficients have two primary functions:
SNP coefficients have two primary functions:
SNP coefficients have two primary functions:
SNP 系数有两个主要功能:
Groups are identified as '[[clique]]s' if every individual is directly tied to every other individual, '[[social circle]]s' if there is less stringency of direct contact, which is imprecise, or as [[Structural cohesion|structurally cohesive]] blocks if precision is wanted.<ref name="uci"/>
The classification of individuals based on their social networking potential, and
根据社交网络潜力对个人进行分类，以及
The weighting of respondents in quantitative marketing research studies.
The weighting of respondents in quantitative marketing research studies.
量化营销研究中受访者的权重。
量化营销研究中受访者的权重。
[[Clustering coefficient]]: A measure of the likelihood that two associates of a node are associates. A higher clustering coefficient indicates a greater 'cliquishness'.<ref>{{cite book|author=Hanneman, Robert A.|author2=Riddle, Mark|name-list-style=amp|chapter=Concepts and Measures for Basic Network Analysis|title=The Sage Handbook of Social Network Analysis|publisher=SAGE|year=2011|isbn=978-1-84787-395-8|pages=346–347|chapter-url=https://books.google.com/books?id=2chSmLzClXgC&pg=PA346}}</ref>
By calculating the SNP of respondents and by targeting High SNP respondents, the strength and relevance of quantitative marketing research used to drive viral marketing strategies is enhanced.
By calculating the SNP of respondents and by targeting High SNP respondents, the strength and relevance of quantitative marketing research used to drive viral marketing strategies is enhanced.
通过计算受访者的单核苷酸多态性，并以高单核苷酸多态性受访者为目标，用于驱动病毒式营销策略的定量营销研究的力量和相关性得到加强。
通过计算受访者的单核苷酸多态性，并以高单核苷酸多态性受访者为目标，用于驱动病毒式营销策略的定量营销研究的力量和相关性得到加强。
Cohesion: The degree to which actors are connected directly to each other by [[Social cohesion|cohesive bonds]]. [[Structural cohesion]] refers to the minimum number of members who, if removed from a group, would disconnect the group.<ref name="asanet"/><ref>{{cite book|author=Pattillo, Jeffrey|chapter=Clique relaxation models in social network analysis|editor1=Thai, My T.|editor2=Pardalos, Panos M.|name-list-style=amp|title=Handbook of Optimization in Complex Networks: Communication and Social Networks|publisher=Springer|year=2011|isbn=978-1-4614-0856-7|page=149|chapter-url=https://books.google.com/books?id=bdRdcHxQQLQC&pg=PA149|display-authors=etal}}</ref>
Variables used to calculate an individual's SNP include but are not limited to: participation in Social Networking activities, group memberships, leadership roles, recognition, publication/editing/contributing to non-electronic media, publication/editing/contributing to electronic media (websites, blogs), and frequency of past distribution of information within their network. The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See Viral Marketing.
Variables used to calculate an individual's SNP include but are not limited to: participation in Social Networking activities, group memberships, leadership roles, recognition, publication/editing/contributing to non-electronic media, publication/editing/contributing to electronic media (websites, blogs), and frequency of past distribution of information within their network. The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See Viral Marketing.
用于计算个人 SNP 的变量包括但不限于: 参与社交网络活动、小组成员身份、领导角色、认可、出版 / 编辑 / 为非电子媒体做贡献、出版 / 编辑 / 为电子媒体(网站、博客)做贡献，以及过去在其网络中发布信息的频率。《广告研究正在改变》(Gerstley，2003)白皮书描述了首字母缩略词“ SNP”和一些最早用于量化个人社交网络潜力的算法。
用于计算个人 SNP 的变量包括但不限于: 参与社交网络活动、小组成员身份、领导角色、认可、出版/编辑/为非电子媒体做贡献、出版/编辑/为电子媒体(网站、博客)做贡献，以及过去在其网络中发布信息的频率。《广告研究正在改变》白皮书(Gerstley，2003)描述了首字母缩略词“ SNP”和一些最早用于量化个人社交网络潜力的算法。
==Modelling and visualization of networks==
Visual representation of social networks is important to understand the network data and convey the result of the analysis.<ref>{{cite journal|url=http://www.cmu.edu/joss/content/articles/volume1/Freeman.html|author=Linton C. Freeman|title=Visualizing Social Networks|journal=Journal of Social Structure|volume=1}}</ref> Numerous methods of visualization for data produced by social network analysis have been presented.<ref>{{cite journal|last=Hamdaqa|first=Mohammad |author2=Tahvildari, Ladan |author3=LaChapelle, Neil |author4=Campbell, Brian|title=Cultural Scene Detection Using Reverse Louvain Optimization|journal=Science of Computer Programming|date=2014|doi=10.1016/j.scico.2014.01.006|volume=95|pages=44–72|url=https://zenodo.org/record/889712 |doi-access=free}}</ref><ref>{{cite conference|author=Bacher, R.|year=1995|title=Graphical Interaction and Visualization for the Analysis and Interpretation of Contingency Analysis Result|conference=Proceedings of the 1995 Power Industry Computer Applications|pages=128–134|location=Salt Lake City, USA|publisher=IEEE Power Engineering Society|doi=10.1109/PICA.1995.515175}}</ref><ref>{{cite journal | last1 = Caschera | first1 = M. C. | last2 = Ferri | first2 = F. | last3 = Grifoni | first3 = P. | year = 2008 | title = SIM: A dynamic multidimensional visualization method for social networks | url = | journal = PsychNology Journal | volume = 6 | issue = 3| pages = 291–320 }}</ref> Many of the [[Social network analysis software|analytic software]] have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.<ref name="interpreting"/>
The first book<ref>Ahonen, T. T., Kasper, T., & Melkko, S. (2005). 3G marketing: communities and strategic partnerships. John Wiley & Sons.</ref> to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book to discuss Alpha Users more generally in the context of [[social marketing intelligence]] was Communities Dominate Brands by Ahonen & Moore in 2005. In 2012, Nicola Greco ([[University College London|UCL]]) presents at [[TEDx]] the Social Networking Potential as a parallelism to the [[potential energy]] that users generate and companies should use, stating that "SNP is the new asset that every company should aim to have".<ref>{{cite web|url=http://tedxtalks.ted.com/video/TEDxMilano-Nicola-Greco-on-math;search%3Atag%3A"technology"|title=Watch "TEDxMilano - Nicola Greco - on math and social network" Video at TEDxTalks|work=TEDxTalks}}</ref>
The first book to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book to discuss Alpha Users more generally in the context of social marketing intelligence was Communities Dominate Brands by Ahonen & Moore in 2005. In 2012, Nicola Greco (UCL) presents at TEDx the Social Networking Potential as a parallelism to the potential energy that users generate and companies should use, stating that "SNP is the new asset that every company should aim to have".
第一本讨论移动电信用户对 Alpha 用户的商业使用的书是2004年由 Ahonen，Kasper 和 Melkko 出版的3 g 营销。第一本在社会营销情报的背景下更广泛地讨论 Alpha 用户的书是 Ahonen & Moore 在2005年出版的《社区主导品牌》。2012年，尼古拉 · 格雷科(UCL)在 TEDx 上发表了《社交网络的潜力》(the Social Networking Potential) ，并将其与用户产生的潜在能源和公司应该使用的能源进行了比较，指出“ SNP 是每个公司都应该致力于拥有的新资产”。
[[Signed graph]]s can be used to illustrate good and bad relationships between humans. A positive edge between two nodes denotes a positive relationship (friendship, alliance, dating) and a negative edge between two nodes denotes a negative relationship (hatred, anger). Signed social network graphs can be used to predict the future evolution of the graph. In signed social networks, there is the concept of "balanced" and "unbalanced" cycles. A balanced cycle is defined as a [[Cycle (graph theory)|cycle]] where the product of all the signs are positive. According to [[balance theory]], balanced graphs represent a group of people who are unlikely to change their opinions of the other people in the group. Unbalanced graphs represent a group of people who are very likely to change their opinions of the people in their group. For example, a group of 3 people (A, B, and C) where A and B have a positive relationship, B and C have a positive relationship, but C and A have a negative relationship is an unbalanced cycle. This group is very likely to morph into a balanced cycle, such as one where B only has a good relationship with A, and both A and B have a negative relationship with C. By using the concept of balanced and unbalanced cycles, the evolution of signed [[social network graph]]s can be predicted.<ref>{{cite journal|author=Cartwright, D. |author2=Frank Harary|year=1956|url=http://snap.stanford.edu/class/cs224w-readings/cartwright56balance.pdf|title=Structural balance: a generalization of Heider's theory|journal=[[Psychological Review]]|volume=63|issue=5|pages=277–293|doi=10.1037/h0046049|pmid=13359597|author2-link=Frank Harary}} Link from [[Stanford University]].
</ref>
Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and mining, network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, social sharing and filtering, recommender systems development, and link prediction and entity resolution. marketing, and business intelligence needs (see social media analytics). Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving.
Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and mining, network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, social sharing and filtering, recommender systems development, and link prediction and entity resolution. marketing, and business intelligence needs (see social media analytics). Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving.
社会网络分析广泛应用于各种应用和学科。一些常见的网络分析应用包括数据聚合和挖掘、网络传播建模、网络建模和抽样、用户属性和行为分析、社区维护的资源支持、基于位置的交互分析、社会共享和过滤、推荐系统开发、链接预测和实体解析。市场营销和商业智能需求(见社交媒体分析)。一些公共部门的使用包括制定领导者参与战略，分析个人和团体参与和媒体使用，以及基于社区的问题解决。
社会网络分析广泛应用于各种应用和学科。一些常见的网络分析应用包括数据聚合和挖掘、网络传播建模、网络建模和抽样、用户属性和行为分析、社区维护的资源支持、基于位置的交互分析、社会共享和过滤、推荐系统开发、链接预测和实体解析。市场营销和商业智能需求(见社交媒体分析)。一些公共部门的应用包括制定领导者参与战略，分析个人和团体参与和媒体使用，以及基于社区的问题解决。
Especially when using social network analysis as a tool for facilitating change, different approaches of participatory network mapping have proven useful. Here participants / interviewers provide network data by actually mapping out the network (with pen and paper or digitally) during the data collection session. An example of a pen-and-paper network mapping approach, which also includes the collection of some actor attributes (perceived influence and goals of actors) is the * [[Net-map toolbox]]. One benefit of this approach is that it allows researchers to collect qualitative data and ask clarifying questions while the network data is collected.<ref name="visualizing"/>
===Social networking potential===
Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map covert organizations such as an espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its clandestine mass electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis. After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network. This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network.
Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map covert organizations such as an espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis. After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network. This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network.
社会网络分析也用于情报、反情报和执法活动。这种技术使分析师能够描绘出隐蔽的组织，如间谍网、有组织犯罪家族或街头帮派。国家安全局(NSA)利用其电子监视程序生成对恐怖分子基层组织和其他被认为与国家安全有关的网络进行此类分析所需的数据。在这次网络分析中，国家安全局查找了三个深层节点。在社会网络的初始映射完成后，进行分析以确定网络的结构，并确定网络中的领导者。这使得军事或执法资产能够对处于领导地位的高价值目标发动抓捕或杀死斩首攻击，以破坏网络的运作。
{{Cleanup|section|reason=More careful cleanup after merge required|date=December 2015}}
The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.
The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.
自911袭击后不久，美国国家安全局就一直在对通话详细记录(cdr)进行社交网络分析，这些记录也被称为元数据。
Social Networking Potential (SNP) is a numeric [[coefficient]], derived through [[algorithm]]s<ref>e.g., Anger, I., & Kittl, C. (2011, September). [http://www.l2f.inesc-id.pt/~fmmb/wiki/uploads/Work/misnis.ref07.pdf Measuring influence on Twitter]. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies (p. 31). ACM.</ref><ref>Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management. 52, p. 949-975.</ref> to represent both the size of an individual's [[social network]] and their ability to influence that network. SNP coefficients were first defined and used by Bob Gerstley in 2002. A closely related term is [[Social marketing intelligence#Alpha users|Alpha User]], defined as a person with a high SNP.
Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes. This automates the approach introduced by Quantitative Narrative Analysis, whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.]]
大型语料库可以转化为网络，然后用社会网络分析的方法进行分析。在这些网络中，节点是社会参与者，链接是行动。这些网络的提取可以通过使用解析器来自动化。由此产生的网络可以包含数千个节点，然后利用网络理论中的工具对其进行分析，以确定关键行为者、关键群体或当事方，以及总体网络的健壮性或结构稳定性或某些节点的中心性等一般性质。这使定量叙事分析引入的方法自动化，即主语-动词-宾语三元组被认定为由动作连接的一对行为者，或者由行为者-宾语形成的一对行为者。]
SNP coefficients have two primary functions:
In other approaches, textual analysis is carried out considering the network of words co-occurring in a text (see for example the Semantic Brand Score). In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).
In other approaches, textual analysis is carried out considering the network of words co-occurring in a text (see for example the Semantic Brand Score). In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).
在其他方法中，考虑到词汇网络在文本中的共现(例如语义品牌得分) ，进行文本分析。在这些网络中，节点是单词，它们之间的链接根据共现频率(在一个特定的最大范围内)进行加权。
#The [[categorization|classification]] of individuals based on their social networking potential, and
#The weighting of [[wikt:respondent|respondents]] in quantitative [[marketing research]] studies.
Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites. The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.
Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites. The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.
社会网络分析也被应用于理解个人、组织和网站之间的在线行为。组织之间的联系已经通过超链接分析来分析问题社区中的哪些组织。
社会网络分析也被应用于理解个人、组织和网站之间的在线行为。组织之间的联系已经通过超链接分析来分析问题社区中的哪些组织。
By calculating the SNP of respondents and by [[Behavioral targeting|targeting]] High SNP respondents, the [[Persuasion|strength]] and [[relevance]] of quantitative marketing research used to drive [[viral marketing]] strategies is enhanced.
Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as [[Twitter]] and [[Facebook]].<ref>{{Cite book|last=Kwak|first=Haewoon|last2=Lee|first2=Changhyun|last3=Park|first3=Hosung|last4=Moon|first4=Sue|date=2010-04-26|title=What is Twitter, a social network or a news media?|publisher=ACM|pages=591–600|doi=10.1145/1772690.1772751|isbn=9781605587998|citeseerx=10.1.1.212.1490}}</ref>
[[Variable (research)|Variables]] used to calculate an individual's SNP include but are not limited to: participation in Social Networking activities, group memberships, leadership roles, recognition, publication/editing/contributing to non-electronic media, publication/editing/contributing to electronic media (websites, blogs), and frequency of past distribution of information within their network. The acronym "SNP" and some of the first algorithms developed to quantify an individual's social networking potential were described in the white paper "Advertising Research is Changing" (Gerstley, 2003) See [[Viral Marketing]].<ref>{{cite book|last1=(Hrsg.)|first1=Sara Rosengren|title=The Changing Roles of Advertising|date=2013|publisher=Springer Fachmedien Wiesbaden GmbH|location=Wiesbaden|isbn=9783658023645|url=https://www.springer.com/us/book/9783658023645|accessdate=22 October 2015}}</ref>
Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as Twitter and Facebook.
Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as Twitter and Facebook.
===In computer-supported collaborative learning===
The first book<ref>Ahonen, T. T., Kasper, T., & Melkko, S. (2005). 3G marketing: communities and strategic partnerships. John Wiley & Sons.</ref> to discuss the commercial use of Alpha Users among mobile telecoms audiences was 3G Marketing by Ahonen, Kasper and Melkko in 2004. The first book to discuss Alpha Users more generally in the context of [[social marketing intelligence]] was Communities Dominate Brands by Ahonen & Moore in 2005. In 2012, Nicola Greco ([[University College London|UCL]]) presents at [[TEDx]] the Social Networking Potential as a parallelism to the [[potential energy]] that users generate and companies should use, stating that "SNP is the new asset that every company should aim to have".<ref>{{cite web|url=http://tedxtalks.ted.com/video/TEDxMilano-Nicola-Greco-on-math;search%3Atag%3A"technology"|title=Watch "TEDxMilano - Nicola Greco - on math and social network" Video at TEDxTalks|work=TEDxTalks}}</ref>
One of the most current methods of the application of SNA is to the study of computer-supported collaborative learning (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication. Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network.
应用国民经济核算体系的最新方法之一是研究电脑支援协作学习。当应用于 CSCL 时，SNA 被用于帮助理解学习者如何在数量、频率和长度方面进行合作，以及交流的质量、主题和策略。此外，系统网络体系结构可以侧重于网络连接的特定方面，或整个网络。它使用图形表示、书面表示和数据表示来帮助检查 CSCL 网络中的连接。
==Practical applications==
{{see also|Social network analysis (criminology)}}
A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence, infrequency of cross-gender interaction in a network, and the relatively small role played by an instructor in an asynchronous learning network.
A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence, infrequency of cross-gender interaction in a network, and the relatively small role played by an instructor in an asynchronous learning network.
许多研究将 SNA 应用于 CSCL，涉及各种情况。研究结果包括网络密度与教师存在之间的相关性、网络中跨性别互动的罕见性以及教师在异步学习网络中扮演的相对较小的角色。
许多研究将 SNA 应用于 CSCL，涉及各种情况。研究结果包括网络密度与教师存在之间的相关性、网络中跨性别互动的罕见性以及教师在异步学习网络中扮演的相对较小的角色。
Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and [[data mining|mining]], network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, [[social sharing]] and filtering, [[recommender system]]s development, and [[link prediction]] and entity resolution.<ref name="Golbeck" /> In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, [[information system]] development analysis,<ref>{{cite journal|last1=Aram|first1=Michael|last2=Neumann|first2=Gustaf|title=Multilayered analysis of co-development of business information systems|journal=Journal of Internet Services and Applications|date=2015-07-01|volume=6|issue=1|doi=10.1186/s13174-015-0030-8|s2cid=16502371|url=http://www.jisajournal.com/content/pdf/s13174-015-0030-8.pdf}}</ref> marketing, and [[business intelligence]] needs (see [[social media analytics]]). Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and [[media use]], and [[Collaborative problem-solving group|community-based problem solving]].
=== Security applications ===
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field, Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field, Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.
虽然许多研究已经证明了社会网络分析在电脑支援协作学习经济领域的价值，研究人员指出，SNA 需要与其他分析方法相辅相成，以形成一个更准确的合作学习经验的图片。
虽然许多研究已经证明了社会网络分析在电脑支援协作学习经济领域的价值，研究人员指出，SNA 需要与其他分析方法相辅相成，以形成一个更准确的合作学习经验的图片。
Social network analysis is also used in intelligence, [[counter-intelligence]] and [[law enforcement]] activities. This technique allows the analysts to map covert organizations such as an [[espionage]] ring, an organized crime family or a street gang. The [[National Security Agency]] (NSA) uses its [[computer surveillance|electronic surveillance]] programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis.<ref name="nsa_degrees">{{cite news|url=https://www.theguardian.com/world/2013/jul/17/nsa-surveillance-house-hearing|date=17 July 2013|accessdate=19 July 2013|title=NSA warned to rein in surveillance as agency reveals even greater scope|newspaper=The Guardian|last1=Ackerman|first1=Spencer}}</ref> After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network.<ref name="nsa_how">{{cite web|url=http://www.digitaltonto.com/2013/how-the-nsa-uses-social-network-analysis-to-map-terrorist-networks/|date=12 June 2013|accessdate=19 Jul 2013|title=How The NSA Uses Social Network Analysis To Map Terrorist Networks}}</ref> This allows military or law enforcement assets to launch capture-or-kill [[decapitation attack]]s on the [[high-value targets]] in leadership positions to disrupt the functioning of the network.
The NSA has been performing social network analysis on [[call detail record]]s (CDRs), also known as [[metadata]], since shortly after the [[September 11 attacks]].<ref name="NSA_SNA">{{cite journal|url=https://www.wired.com/science/discoveries/news/2006/05/70888|title=NSA Using Social Network Analysis|journal=Wired|date=12 May 2006|accessdate=19 July 2013}}</ref><ref name="nsa_usa">{{cite web|url=http://www.slate.com/articles/news_and_politics/explainer/2006/05/how_the_nsa_does_social_network_analysis.html|date=11 May 2006|accessdate=19 July 2013|title=NSA has massive database of Americans' phone calls }}</ref>
=== Textual analysis applications ===
Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.<ref>{{cite journal|vauthors=Sudhahar S, De Fazio G, Franzosi R, Cristianini N|year=2013|title=Network analysis of narrative content in large corpora|journal=Natural Language Engineering|volume=21|issue=1|pages=1–32|doi=10.1017/S1351324913000247|url=https://research-information.bristol.ac.uk/en/publications/network-analysis-of-narrative-content-in-large-corpora(dfb87140-42e2-486a-91d5-55f9007042df).html|hdl=1983/dfb87140-42e2-486a-91d5-55f9007042df}}</ref> This automates the approach introduced by Quantitative Narrative Analysis,<ref>Quantitative Narrative Analysis; Roberto Franzosi; Emory University © 2010</ref> whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.<ref name="ReferenceA" />
[[File:Tripletsnew2012.png|thumb|right|Narrative network of US Elections 2012<ref name="ReferenceA">{{cite journal|title=Automated analysis of the US presidential elections using Big Data and network analysis|vauthors=Sudhahar S, Veltri GA, Cristianini N|journal=Big Data & Society|volume=2|issue=1|pages=1–28|year=2015|doi=10.1177/2053951715572916|doi-access=free}}</ref>]]
In other approaches, textual analysis is carried out considering the network of words co-occurring in a text (see for example the [[Semantic Brand Score]]). In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).
=== Internet applications ===
Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites.<ref name=Ghanbarnejad/> [[Hyperlink]] analysis can be used to analyze the connections between [[website]]s or [[Web page|webpages]] to examine how information flows as individuals navigate the web.<ref>{{Cite journal|last1=OSTERBUR|first1=MEGAN|last2=KIEL|first2=CHRISTINA|date=2016-05-02|title=A hegemon fighting for equal rights: the dominant role of COC Nederland in the LGBT transnational advocacy network|journal=Global Networks|language=en|volume=17|issue=2|pages=234–254|doi=10.1111/glob.12126|issn=1470-2266}}</ref> The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.<ref>Osterbur, Megan E. and Christina Kiel. "Pink Links: Visualizing the Global LGBTQ Network" in ''LGBTQ Politics: A Critical Reader''. eds. Marla Brettschneider, Susan Burgess, Christine Keating. pg493-522</ref>
==== Social Media Internet Applications ====
==See also==
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Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as [[Twitter]] and [[Facebook]].<ref>{{Cite book|last1=Kwak|first1=Haewoon|last2=Lee|first2=Changhyun|last3=Park|first3=Hosung|last4=Moon|first4=Sue|date=2010-04-26|title=What is Twitter, a social network or a news media?|publisher=ACM|pages=591–600|doi=10.1145/1772690.1772751|isbn=9781605587998|citeseerx=10.1.1.212.1490|s2cid=207178765}}</ref>
==External links==
===In computer-supported collaborative learning===
{{external links|date=January 2017}}
One of the most current methods of the application of SNA is to the study of [[computer-supported collaborative learning]] (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication.<ref name=":0">{{Cite journal|last1=Laat|first1=Maarten de|last2=Lally|first2=Vic|last3=Lipponen|first3=Lasse|last4=Simons|first4=Robert-Jan|date=2007-03-08|title=Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis|journal=International Journal of Computer-Supported Collaborative Learning|language=en|volume=2|issue=1|pages=87–103|doi=10.1007/s11412-007-9006-4|s2cid=3238474}}</ref> Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network.<ref name=":0" /> When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own.
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====Key terms====
| PLEASE BE CAUTIOUS IN ADDING MORE LINKS TO THIS ARTICLE. Wikipedia |
| PLEASE BE CAUTIOUS IN ADDING MORE LINKS TO THIS ARTICLE. Wikipedia |
请谨慎添加更多链接到本文。维基百科 |
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There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: '''density''', '''centrality''', '''indegree''', '''outdegree''', and '''sociogram'''.
| is not a collection of links nor should it be used for advertising. |
| is not a collection of links nor should it be used for advertising. |
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* '''Density''' refers to the "connections" between participants. Density is defined as the number of connections a participant has, divided by the total possible connections a participant could have. For example, if there are 20 people participating, each person could potentially connect to 19 other people. A density of 100% (19/19) is the greatest density in the system. A density of 5% indicates there is only 1 of 19 possible connections.<ref name=":0" />
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| Excessive or inappropriate links WILL BE DELETED. |
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* '''Centrality''' focuses on the behavior of individual participants within a network. It measures the extent to which an individual interacts with other individuals in the network. The more an individual connects to others in a network, the greater their centrality in the network.<ref name=":0" /><ref name=":1" />
| See Wikipedia:External links & Wikipedia:Spam for details. |
| See Wikipedia:External links & Wikipedia:Spam for details. |
查看维基百科: 外部链接和维基百科: 垃圾信息。|
查看维基百科: 外部链接和维基百科: 垃圾信息。|
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In-degree and out-degree variables are related to centrality.
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| If there are already plentiful links, please propose additions or |
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更新本文讨论页面，或者提交你的链接
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* '''In-degree''' centrality concentrates on a specific individual as the point of focus; centrality of all other individuals is based on their relation to the focal point of the "in-degree" individual.<ref name=":0" />
| to the relevant category at the Open Directory Project (dmoz.org) |
| to the relevant category at the Open Directory Project (dmoz.org) |
| 相关类别的 dmoz (dmoz. org) |
| 相关类别的 dmoz (dmoz.org) |
* '''Out-degree''' is a measure of centrality that still focuses on a single individual, but the analytic is concerned with the out-going interactions of the individual; the measure of out-degree centrality is how many times the focus point individual interacts with others.<ref name=":0" /><ref name=":1" />
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* A '''sociogram''' is a visualization with defined boundaries of connections in the network. For example, a sociogram which shows out-degree centrality points for Participant A would illustrate all outgoing connections Participant A made in the studied network.<ref name=":0" />
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=== Further reading ===
====Unique capabilities====
Researchers employ social network analysis in the study of computer-supported collaborative learning in part due to the unique capabilities it offers. This particular method allows the study of interaction patterns within a [[Networked learning|networked learning community]] and can help illustrate the extent of the participants' interactions with the other members of the group.<ref name=":0" /> The graphics created using SNA tools provide visualizations of the connections among participants and the strategies used to communicate within the group. Some authors also suggest that SNA provides a method of easily analyzing changes in participatory patterns of members over time.<ref>{{cite conference|author1=Palonen, T.|author2= Hakkarainen, K. B.|name-list-style=amp|editor1=Fishman|editor2=S. O'Connor-Divelbiss|title=Patterns of Interaction in Computer-Supported Learning: A Social Network Analysis|conference=Fourth International Conference of the Learning Sciences|location=Mahwah, NJ|publisher=Erlbaum|pp=334–339|url=http://www.umich.edu/~icls/proceedings/pdf/Palonen.pdf}}</ref>
A number of research studies have applied SNA to CSCL across a variety of contexts. The findings include the correlation between a network's density and the teacher's presence,<ref name=":0" /> a greater regard for the recommendations of "central" participants,<ref>{{Cite journal|last1=Martínez|first1=A.|last2=Dimitriadis|first2=Y.|last3=Rubia|first3=B.|last4=Gómez|first4=E.|last5=de la Fuente|first5=P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education|series=Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474}}</ref> infrequency of cross-gender interaction in a network,<ref>{{cite conference|author1=Cho, H.|author2=Stefanone, M.|author3=Gay, G|name-list-style=amp|year=2002|title=Social information sharing in a CSCL community|conference=Computer support for collaborative learning: Foundations for a CSCL community|location=Hillsdale, NJ|publisher=Lawrence Erlbaum|pages=43–50|citeseerx=10.1.1.225.5273}}</ref> and the relatively small role played by an instructor in an [[asynchronous learning]] network.<ref>{{cite journal|author1=Aviv, R.|author2=Erlich, Z.|author3=Ravid, G.|author4=Geva, A.|name-list-style=amp|year=2003|title=Network analysis of knowledge construction in asynchronous learning networks|journal=Journal of Asynchronous Learning Networks|volume=7|issue=3|pp=1–23|citeseerx=10.1.1.2.9044}}</ref>
====Other methods used alongside SNA====
Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,<ref name=":0" /> researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL. The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL.<ref>{{Cite book|title=Groupware: Design, Implementation, and Use|last1=Daradoumis|first1=Thanasis|last2=Martínez-Monés|first2=Alejandra|last3=Xhafa|first3=Fatos|date=2004-09-05|publisher=Springer Berlin Heidelberg|isbn=9783540230168|editor-last=Vreede|editor-first=Gert-Jan de|series=Lecture Notes in Computer Science|pages=[https://archive.org/details/unset0000unse_i0a6/page/289 289–304]|language=en|doi=10.1007/978-3-540-30112-7_25|editor-last2=Guerrero|editor-first2=Luis A.|editor-last3=Raventós|editor-first3=Gabriela Marín|hdl=2117/116654|url-access=registration|url=https://archive.org/details/unset0000unse_i0a6/page/289}}</ref> Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.<ref name=autogenerated1/>
A number of research studies have combined other types of analysis with SNA in the study of CSCL. This can be referred to as a multi-method approach or data [[Triangulation (social science)|triangulation]], which will lead to an increase of evaluation [[Reliability (statistics)|reliability]] in CSCL studies.
* Qualitative method – The principles of qualitative case study research constitute a solid framework for the integration of SNA methods in the study of CSCL experiences.<ref>{{Cite journal|last=Johnson|first=Karen E.|date=1996-01-01|title=Review of The Art of Case Study Research|jstor=329758|journal=The Modern Language Journal|volume=80|issue=4|pages=556–557|doi=10.2307/329758}}</ref>
**''[[Ethnography|Ethnographic]] data'' such as student questionnaires and interviews and classroom non-participant observations<ref name=autogenerated1>{{cite journal|author1=Martínez, A.|author2=Dimitriadis, Y.|author3=Rubia, B.|author4=Gómez, E.|author5=de la Fuente, P.|date=2003-12-01|title=Combining qualitative evaluation and social network analysis for the study of classroom social interactions|journal=Computers & Education. Documenting Collaborative Interactions: Issues and Approaches|volume=41|issue=4|pages=353–368|doi=10.1016/j.compedu.2003.06.001|citeseerx=10.1.1.114.7474}}</ref>
===Peer-reviewed journals===
**''[[Case study|Case studies]]'': comprehensively study particular CSCL situations and relate findings to general schemes<ref name=autogenerated1 />
* ''[http://www.sciencedirect.com/science/journal/03788733 Social Networks]''
**''[[Content analysis]]:'' offers information about the content of the communication among members<ref name=autogenerated1 />
* ''[http://journals.cambridge.org/action/displayJournal?jid=nws Network Science]''
* Quantitative method – This includes simple descriptive statistical analyses on occurrences to identify particular attitudes of group members who have not been able to be tracked via SNA in order to detect general tendencies.
* ''[http://www.cmu.edu/joss/content/articles/volindex.html Journal of Social Structure]''
**''Computer [[Logfile|log files]]:'' provide automatic data on how collaborative tools are used by learners<ref name=autogenerated1 />
* ''[http://comnet.oxfordjournals.org/ Journal of Complex Networks]''
**''[[Multidimensional scaling|Multidimensional scaling (MDS)]]'': charts similarities among actors, so that more similar input data is closer together<ref name=autogenerated1 />
* ''[http://www.tandfonline.com/toc/gmas20/current Journal of Mathematical Sociology]''
**''[[Software]] tools:'' QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST<ref name=autogenerated1 />
==See also==
{{div col|colwidth=22em}}
* [[Actor-network theory]]
* [[Community structure]]
* ''[http://www.cs.cornell.edu/home/kleinber/networks-book/ Networks, Crowds, and Markets]'' (2010) by D. Easley & J. Kleinberg
* [[Complex network]]
* ''[http://faculty.ucr.edu/~hanneman/nettext/ Introduction to Social Networks Methods]'' (2005) by R. Hanneman & M. Riddle
* [[Digital humanities]]
* ''[http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118169476.html Social Network Analysis with Applications]'' (2013) by I. McCulloh, H. Armstrong & A. Johnson
* [[Dynamic network analysis]]
* ''[https://www.wiley.com/en-us/Social+Network+Analysis+in+Telecommunications-p-9780470647547 Social Network Analysis in Telecommunications]'' (2011) by Carlos Andre Reis Pinheiro
* [[Friendship paradox]]
* [[Individual mobility]]
* [[Mathematical sociology]]
* [[Metcalfe's law]]
* [[Network-based diffusion analysis]]
* [[Network science]]
* [[Organizational patterns]]
* [[Small world phenomenon]]
* [[Social media analytics]]
* [[Social media mining]]
[[Category:Social networks]]
Category:Social networks
Category:Social networks
分类: 社交网络
分类: 社交网络
[[Category:Value (ethics)]]
* [[Social network]]
Category:Value (ethics)
Category:Value (ethics)
类别: 价值(道德)
类别: 价值(道德)
[[Category:Systems theory]]
* [[Social network analysis software]]
Category:Systems theory
Category:Systems theory
范畴: 系统论
范畴: 系统论
[[Category:Social systems]]
* [[Social networking service]]
Category:Social systems
Category:Social systems
类别: 社会系统
类别: 社会系统
[[Category:Self-organization]]
* [[Social software]]
Category:Self-organization
Category:Self-organization
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类别: 自我组织
[[Category:Community building]]
* [[Social web]]
Category:Community building
Category:Community building
类别: 社区建设
类别: 社区建设
[[Category:Cultural economics]]
* [[Sociomapping]]
Category:Cultural economics
Category:Cultural economics
类别: 文化经济学
类别: 文化经济学
[[Category:Social information processing]]
* [[Attention inequality]]
Category:Social information processing
Category:Social information processing
类别: 社会信息处理
类别: 社会信息处理
{{div col end}}
Category:Surveillance
Category:Surveillance
类别: 监控
类别: 监控
Category:Methods in sociology
Category:Methods in sociology
范畴: 社会学方法
范畴: 社会学方法
==References==
Category:Internet culture
Category:Internet culture