社交网络分析

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模板:Sociology模板:Network science

文件:Kencf0618FacebookNetwork.jpg
A social network diagram displaying friendship ties among a set of Facebook users.

social network diagram displaying friendship ties among a set of Facebook users.]]

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 networks and graph theory.[1] 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,[2][3] memes spread,[4] information circulation,[5] friendship and acquaintance networks, business networks, knowledge networks,[6][7] difficult working relationships,[8] social networks, collaboration graphs, kinship, disease transmission, and sexual relationships.[9][10] 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.[11]

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,[12] demography, communication studies,[3][13] economics, geography, history, information science, organizational studies,[6][8] political science, public health,[14][7] social psychology, development studies, sociolinguistics, and computer science[15] and is now commonly available as a consumer tool (see the list of SNA software).[16][17][18][19]

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).

社会网络分析已经成为现代社会学的一项关键技术。它在人类学、生物学、人口学、通讯研究、经济学、地理学、历史学、信息科学、组织研究方面也获得了大量的追随者,现在通常作为消费者工具使用(见 SNA 软件清单)。


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 Jennings introduced basic analytical methods.[20] 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.[21] Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,[22] Wouter De Nooy,[23] and Burgert Senekal.[24] 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 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。


Metrics

文件:Graph betweenness.svg
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[[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

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.[25] 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.[26] For example, two people who are friends and also work together would have a multiplexity of 2.[27] Multiplexity has been associated with relationship strength and can also comprise overlap of positive and negative network ties.[8]

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.

多样性: 一条领带所包含的内容形式的数量。复杂性与关系强度有关,也可能包含正面和负面网络关系的重叠。


Mutuality/Reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction.[28]

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。


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.[29]

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.[28]

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

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。

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.[30]


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。

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.[31][32][33][34] Examples of common methods of measuring "centrality" include betweenness centrality,[35] closeness centrality, eigenvector centrality, alpha centrality, and degree centrality.[36]


Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).

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)。

Density: The proportion of direct ties in a network relative to the total number possible.[37][38]


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。

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 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.

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).[30] 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 系数有两个主要功能:

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.[39]

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.

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'.[40]


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。

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.[41][42]


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》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.[43] Numerous methods of visualization for data produced by social network analysis have been presented.[44][45][46] 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.[47]

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 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.[48]


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.[49]


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 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。

模板:Cleanup

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 algorithms[50][51] 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.


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).

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。

  1. The classification of individuals based on their social networking potential, and
  1. The weighting of 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.

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 targeting High SNP respondents, the strength and relevance of quantitative marketing research used to drive viral marketing strategies is enhanced.


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.[52]

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.

社交网络分析已经被应用到社交媒体上,作为一种工具,通过它们在社交媒体网站(如 Twitter 和 Facebook)上的链接来了解个人或组织之间的行为。


The first book[53] 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".[54]


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

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。

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.[55] In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, information system development analysis,[56] 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.


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.

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 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.[57] 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.[58] 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.

The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.[59][60]


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.[61] This automates the approach introduced by Quantitative Narrative Analysis,[62] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.[63]

Narrative network of US Elections 2012[63]

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.[15] Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web.[64] The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.[65]


Social Media Internet Applications

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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.[66]


In computer-supported collaborative learning

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.[67] 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.[67] 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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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 community and can help illustrate the extent of the participants' interactions with the other members of the group.[67] 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.[68]


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,[67] a greater regard for the recommendations of "central" participants,[69] infrequency of cross-gender interaction in a network,[70] and the relatively small role played by an instructor in an asynchronous learning network.[71]


Other methods used alongside SNA

Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field,[67] 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.[72] Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.[73]


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.


  • 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.[74]
    • Ethnographic data such as student questionnaires and interviews and classroom non-participant observations[73]
    • Case studies: comprehensively study particular CSCL situations and relate findings to general schemes[73]
  • 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 log files: provide automatic data on how collaborative tools are used by learners[73]
    • Software tools: QUEST, SAMSA (System for Adjacency Matrix and Sociogram-based Analysis), and Nud*IST[73]


See also

Category:Social networks

分类: 社交网络

Category:Value (ethics)

类别: 价值(道德)

Category:Systems theory

范畴: 系统论

Category:Social systems

类别: 社会系统

Category:Self-organization

类别: 自我组织

Category:Community building

类别: 社区建设

Category:Cultural economics

类别: 文化经济学

Category:Social information processing

类别: 社会信息处理

Category:Surveillance

类别: 监控


Category:Methods in sociology

范畴: 社会学方法

References

Category:Internet culture

类别: 互联网文化


This page was moved from wikipedia:en:Social network analysis. Its edit history can be viewed at 社交网络分析/edithistory

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