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第319行： − '''[[Social network]] analysis''' examines the structure of relationships between social entities.<ref name="Wasserman_Faust">[[Wasserman, Stanley]] and Katherine Faust. 1994. ''Social Network Analysis: Methods and Applications.'' Cambridge: Cambridge University Press. − </ref> These entities are often persons, but may also be [[Group (sociology)|groups]], [[organizations]], [[nation states]], [[web sites]], [[scientometrics|scholarly publications]]. − 社会网络分析考察了社会实体之间的关系结构。[27]这些实体通常是个人，但也可能是团体、组织、国家、网站、学术出版物。+ + − Since the 1970s, the empirical study of networks has played a central role in social science, and many of the [[Mathematics|mathematical]] and [[Statistics|statistical]] tools used for studying networks have been first developed in [[sociology]].<ref name="Newman">Newman, M.E.J. ''Networks: An Introduction.'' Oxford University Press. 2010, {{ISBN|978-0199206650}}</ref> Amongst many other applications, social network analysis has been used to understand the [[diffusion of innovations]], news and rumors. Similarly, it has been used to examine the spread of both [[epidemiology|diseases]] and [[Medical sociology|health-related behaviors]]. It has also been applied to the [[Economic sociology|study of markets]], where it has been used to examine the role of trust in [[Social exchange|exchange relationships]] and of social mechanisms in setting prices. Similarly, it has been used to study recruitment into [[political movement]]s and social organizations. It has also been used to conceptualize scientific disagreements as well as academic prestige. More recently, network analysis (and its close cousin [[traffic analysis]]) has gained a significant use in military intelligence, for uncovering insurgent networks of both hierarchical and [[leaderless resistance|leaderless]] nature.<ref name=GT-33/><ref>{{Cite web |url=http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/941/863 |title=Network analysis of terrorist networks |access-date=2011-12-12 |archive-url=https://web.archive.org/web/20121123010939/http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/941/863 |archive-date=2012-11-23 |url-status=dead }}</ref>In [[Social network analysis (criminology)|criminology]], it is being used to identify influential actors in criminal gangs, offender movements, co-offending, predict criminal activities and make policies.<ref>{{Cite journal|last=PhD|first=Martin Bouchard|last2=PhD|first2=Aili Malm|date=2016-11-02|title=Social Network Analysis and Its Contribution to Research on Crime and Criminal Justice|url=https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199935383.001.0001/oxfordhb-9780199935383-e-21|language=en|doi=10.1093/oxfordhb/9780199935383.013.21}}</ref>+ − 自20世纪70年代以来，对网络的实证研究在社会科学发挥了核心作用，许多用于研究网络的数学和统计工具最早是在社会学中发展起来的。[28]在众多的应用中，社会网络分析被用来分析产品创新、新闻和谣言的扩散机制，同时也被用来检测疾病和与健康相关的行为的传播。它也被应用于市场研究，主要用于分析信任在交换关系中的作用以及社会机制在市场定价中的作用。同样，它也可以用于研究政治运动和社会组织的招募问题，以及概念化科学分歧和学术声望。最近，网络分析（及其相近概念流量分析）在军事情报中得到了重要的应用，用于揭露具有层级性和无领导性本质的叛乱者网络。在犯罪学中，它也被用来识别犯罪团伙、犯罪活动、共同犯罪中有影响力的角色，预测犯罪活动以及制定相应的政策。[31] − + − 动态网络分析考察了在复杂的社会技术系统影响下，不同阶级的实体之间关系的转变结构，反映了社会的稳定和变化，如新群体、主题和领导者的出现。动态网络分析的重点是由多种类型的节点（实体）和多种类型的链接组成的元网络。这些实体可以非常多样化，包括人员、组织、主题、资源、任务、事件、地点，甚至是信仰。 + − Dynamic network techniques are particularly useful for assessing trends and changes in networks over time, identification of emergent leaders, and examining the co-evolution of people and ideas. − 动态网络技术是非常有用的一种方法，特别是对于评估网络随时间变化的趋势和变化，识别新兴领导者，以及检查人与思想的共同进化等方面。+ − ===生物网络分析===+ − With the recent explosion of publicly available high throughput biological data, the analysis of molecular networks has gained significant interest. The type of analysis in this content are closely related to social network analysis, but often focusing on local patterns in the network. For example, [[network motif]]s are small subgraphs that are over-represented in the network. [[Activity motifs]] are similar over-represented patterns in the attributes of nodes and edges in the network that are over represented given the network structure. The analysis of [[biological network]]s has led to the development of [[network medicine]], which looks at the effect of diseases in the [[interactome]].<ref>{{cite journal | last1 = Barabási | first1 = A. L. | last2 = Gulbahce | first2 = N. | last3 = Loscalzo | first3 = J. | year = 2011 | title = Network medicine: a network-based approach to human disease | journal = Nature Reviews Genetics | volume = 12 | issue = 1| pages = 56–68 | doi=10.1038/nrg2918 | pmid=21164525 | pmc=3140052}}</ref> − 随着近年来公开的生物学数据的爆炸性增长，分子网络分析引起了人们极大的兴趣。这种分析类型与社会网络分析密切相关，但通常更侧重于网络中的局部模式。例如网络模体（network motifs）是指网络中过表达的小的子图。在给定网络结构的情况下，网络中节点和连边的属性中的活动模体是相似的过表达模式。生物网络的分析促进了网络医学的发展，网络医学主要是从交互中观察疾病的影响。[33] − Link analysis is a subset of network analysis, exploring associations between objects. An example may be examining the addresses of suspects and victims, the telephone numbers they have dialed and financial transactions that they have partaken in during a given timeframe, and the familial relationships between these subjects as a part of police investigation. Link analysis here provides the crucial relationships and associations between very many objects of different types that are not apparent from isolated pieces of information. Computer-assisted or fully automatic computer-based link analysis is increasingly employed by [[bank]]s and [[insurance]] agencies in [[fraud]] detection, by telecommunication operators in telecommunication network analysis, by medical sector in [[epidemiology]] and [[pharmacology]], in law enforcement [[Criminal procedure|investigation]]s, by [[search engine]]s for [[relevance]] rating (and conversely by the [[search engine spammer|spammers]] for [[spamdexing]] and by business owners for [[search engine optimization]]), and everywhere else where relationships between many objects have to be analyzed. 第352行：
第346行： − The structural robustness of networks<ref>{{cite book |title= Complex Networks: Structure, Robustness and Function |author1=R. Cohen |author2=S. Havlin |year= 2010 |publisher= Cambridge University Press |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_com_net.php}}</ref> is studied using [[percolation theory]]. When a critical fraction of nodes is removed the network becomes fragmented into small clusters. This phenomenon is called percolation<ref>{{cite book |title= Fractals and Disordered Systems |author1=A. Bunde |author2=S. Havlin |year= 1996 |publisher= Springer |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_fds.php}}</ref> and it represents an order-disorder type of [[phase transition]] with [[critical exponents]]. − + + − The [[SIR model]] is one of the most well known algorithms on predicting the spread of global pandemics within an infectious population. 第365行：
第358行： − The formula above describes the "force" of infection for each susceptible unit in an infectious population, where {{math|β}} is equivalent to the transmission rate of said disease. − − To track the change of those susceptible in an infectious population: + 第380行：
第371行： − Over time, the number of those infected fluctuates by: the specified rate of recovery, represented by <math>\mu</math> but deducted to one over the average infectious period <math>{1\over \tau}</math>, the numbered of infectious individuals, <math>I</math>, and the change in time, <math>\Delta t</math>. + − Whether a population will be overcome by a pandemic, with regards to the SIR model, is dependent on the value of <math>R_0</math> or the "average people infected by an infected individual." + − Several [[Web search]] [[ranking]] algorithms use link-based centrality metrics, including (in order of appearance) [[Massimo Marchiori|Marchiori]]'s [[Hyper Search]], [[Google]]'s [[PageRank]], Kleinberg's [[HITS algorithm]], the [[CheiRank]] and [[TrustRank]] algorithms. Link analysis is also conducted in information science and communication science in order to understand and extract information from the structure of collections of web pages. For example, the analysis might be of the interlinking between politicians' web sites or blogs. 第398行：
第388行： − − [[PageRank]] works by randomly picking "nodes" or websites and then with a certain probability, "randomly jumping" to other nodes. By randomly jumping to these other nodes, it helps PageRank completely traverse the network as some webpages exist on the periphery and would not as readily be assessed. − − Each node, <math>x_i</math>, has a PageRank as defined by the sum of pages <math>j</math> that link to <math>i</math> times one over the outlinks or "out-degree" of <math>j</math> times the "importance" or PageRank of <math>j</math>. 第407行：
第393行： + − As explained above, PageRank enlists random jumps in attempts to assign PageRank to every website on the internet. These random jumps find websites that might not be found during the normal search methodologies such as [[Breadth-First Search]] and [[Depth-First Search]]. − − In an improvement over the aforementioned formula for determining PageRank includes adding these random jump components. Without the random jumps, some pages would receive a PageRank of 0 which would not be good. − − The first is <math>\alpha</math>, or the probability that a random jump will occur. Contrasting is the "damping factor", or <math>1 - \alpha</math>. 第425行：
第407行： − Another way of looking at it:+ + − 从另一个角度来看 − : <math>R(A) = \sum {R_B\over B_\text{(outlinks)}} + \cdots + {R_n \over n_\text{(outlinks)}}</math> − Information about the relative importance of nodes and edges in a graph can be obtained through [[centrality]] measures, widely used in disciplines like [[sociology]]. Centrality measures are essential when a network analysis has to answer questions such as: "Which nodes in the network should be targeted to ensure that a message or information spreads to all or most nodes in the network?" or conversely, "Which nodes should be targeted to curtail the spread of a disease?". Formally established measures of centrality are [[degree centrality]], [[closeness centrality]], [[betweenness centrality]], [[eigenvector centrality]], and [[katz centrality]]. The objective of network analysis generally determines the type of centrality measure(s) to be used.<ref name="Wasserman_Faust"/> − − *'''Degree centrality''' of a node in a network is the number of links (vertices) incident on the node. − − − *'''Closeness centrality''' determines how "close" a node is to other nodes in a network by measuring the sum of the shortest distances (geodesic paths) between that node and all other nodes in the network. − − − *'''Betweenness centrality''' determines the relative importance of a node by measuring the amount of traffic flowing through that node to other nodes in the network. This is done by measuring the fraction of paths connecting all pairs of nodes and containing the node of interest. Group Betweenness centrality measures the amount of traffic flowing through a group of nodes.<ref>{{cite journal | last1 = Puzis | first1 = R. | last2 = Yagil | first2 = D. | last3 = Elovici | first3 = Y. | last4 = Braha | first4 = D. | year = 2009 | title = Collaborative attack on Internet users' anonymity | url = http://necsi.edu/affiliates/braha/Internet_Research_Anonimity.pdf | journal = Internet Research | volume = 19 | issue = | page = 1 | doi = 10.1108/10662240910927821 | citeseerx = 10.1.1.219.3949 | access-date = 2015-02-08 | archive-url = https://web.archive.org/web/20131207133417/http://necsi.edu/affiliates/braha/Internet_Research_Anonimity.pdf | archive-date = 2013-12-07 | url-status = dead }}</ref> − − − *'''Eigenvector centrality''' is a more sophisticated version of degree centrality where the centrality of a node not only depends on the number of links incident on the node but also the quality of those links. This quality factor is determined by the eigenvectors of the adjacency matrix of the network. − − − *'''Katz centrality''' of a node is measured by summing the geodesic paths between that node and all (reachable) nodes in the network. These paths are weighted, paths connecting the node with its immediate neighbors carry higher weights than those which connect with nodes farther away from the immediate neighbors.
→网络分析
==网络分析==
==网络分析==
===社会网络分析===
===社会网络分析===
'''[[社会网络]]'''分析考察了社会实体之间的关系结构。<ref name="Wasserman_Faust">[[Wasserman, Stanley]] and Katherine Faust. 1994. ''Social Network Analysis: Methods and Applications.'' Cambridge: Cambridge University Press.
</ref>这些实体通常是个人，但也可能是团体、组织、国家、网站、学术出版物。
自20世纪70年代以来，对网络的实证研究在社会科学发挥了核心作用，许多用于研究网络的数学和统计工具最早是在社会学中发展起来的。<ref name="Newman">Newman, M.E.J. ''Networks: An Introduction.'' Oxford University Press. 2010, {{ISBN|978-0199206650}}</ref> 在众多的应用中，社会网络分析被用来分析产品创新、新闻和谣言的扩散机制，同时也被用来检测疾病和与健康相关的行为的传播。它也被应用于市场研究，主要用于分析信任在交换关系中的作用以及社会机制在市场定价中的作用。同样，它也可以用于研究政治运动和社会组织的招募问题，以及概念化科学分歧和学术声望。最近，网络分析（及其相近概念流量分析）在军事情报中得到了重要的应用，用于揭露具有层级性和无领导性本质的叛乱者网络。<ref name=GT-33/><ref>{{Cite web |url=http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/941/863 |title=Network analysis of terrorist networks |access-date=2011-12-12 |archive-url=https://web.archive.org/web/20121123010939/http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/941/863 |archive-date=2012-11-23 |url-status=dead }}</ref>在犯罪学中，它也被用来识别犯罪团伙、犯罪活动、共同犯罪中有影响力的角色，预测犯罪活动以及制定相应的政策。<ref>{{Cite journal|last=PhD|first=Martin Bouchard|last2=PhD|first2=Aili Malm|date=2016-11-02|title=Social Network Analysis and Its Contribution to Research on Crime and Criminal Justice|url=https://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199935383.001.0001/oxfordhb-9780199935383-e-21|language=en|doi=10.1093/oxfordhb/9780199935383.013.21}}</ref>
===动态网络分析===
===动态网络分析===
[[Dynamic network analysis]] examines the shifting structure of relationships among different classes of entities in complex socio-technical systems effects, and reflects social stability and changes such as the emergence of new groups, topics, and leaders.<ref name="dynamic1"/><ref name="dynamic2"/><ref name="dynamic3"/><ref name="dynamic4"/><ref>Xanthos, Aris, Pante, Isaac, Rochat, Yannick, Grandjean, Martin (2016). [http://dh2016.adho.org/abstracts/407 Visualising the Dynamics of Character Networks]. In Digital Humanities 2016: Jagiellonian University & Pedagogical University, Kraków, pp. 417–419.</ref> Dynamic Network Analysis focuses on meta-networks composed of multiple types of nodes (entities) and [[Multidimensional network|multiple types of links]]. These entities can be highly varied.<ref name="dynamic1"/> Examples include people, organizations, topics, resources, tasks, events, locations, and beliefs.
[[动态网络分析]]考察了在复杂的社会技术系统影响下，不同阶级的实体之间关系的转变结构，反映了社会的稳定和变化，如新群体、主题和领导者的出现。<ref name="dynamic1"/><ref name="dynamic2"/><ref name="dynamic3"/><ref name="dynamic4"/><ref>Xanthos, Aris, Pante, Isaac, Rochat, Yannick, Grandjean, Martin (2016). [http://dh2016.adho.org/abstracts/407 Visualising the Dynamics of Character Networks]. In Digital Humanities 2016: Jagiellonian University & Pedagogical University, Kraków, pp. 417–419.</ref> 动态网络分析的重点是由多种类型的节点（实体）和多种类型的链接组成的元网络。这些实体可以非常多样化，包括人员、组织、主题、资源、任务、事件、地点，甚至是信仰。
动态网络技术是非常有用的一种方法，特别是对于评估网络随时间变化的趋势和变化，识别新兴领导者，以及检查人与思想的共同进化等方面。
===生物网络分析===
随着近年来公开的生物学数据的爆炸性增长，分子网络分析引起了人们极大的兴趣。这种分析类型与社会网络分析密切相关，但通常更侧重于网络中的局部模式。例如网络模体 network motifs是指网络中过表达的小的子图。在给定网络结构的情况下，网络中节点和连边的属性中的活动模体是相似的过表达模式。生物网络的分析促进了网络医学的发展，网络医学主要是从交互中观察疾病的影响。<ref>{{cite journal | last1 = Barabási | first1 = A. L. | last2 = Gulbahce | first2 = N. | last3 = Loscalzo | first3 = J. | year = 2011 | title = Network medicine: a network-based approach to human disease | journal = Nature Reviews Genetics | volume = 12 | issue = 1| pages = 56–68 | doi=10.1038/nrg2918 | pmid=21164525 | pmc=3140052}}</ref>
===链路分析===
===链路分析===
链路分析是网络分析的一个子集，主要是探索对象之间的联系。例如，作为警方调查的一部分，可以分析嫌疑人和受害人的地址、他们所拨打过的电话号码、他们在一段时间内参与的金融交易，以及这些对象之间的家庭关系。链路分析提供了许多不同类型的对象之间的关键关系和相互联系，而这些在孤立的信息片段中是看不出来的。计算机辅助或全自动计算机链路分析越来越多地被应用在诸多领域，例如银行和保险机构用来进行欺诈检测，电信运营商用来分析电信网络，医疗部门用来分析流行病学和药理学，执法调查，搜索引擎用于相关性评级（反之，垃圾邮件发送者用于垃圾邮件发送，或业务负责人用来优化搜索引擎）以及进行任何有联系的对象之间的分析。
链路分析是网络分析的一个子集，主要是探索对象之间的联系。例如，作为警方调查的一部分，可以分析嫌疑人和受害人的地址、他们所拨打过的电话号码、他们在一段时间内参与的金融交易，以及这些对象之间的家庭关系。链路分析提供了许多不同类型的对象之间的关键关系和相互联系，而这些在孤立的信息片段中是看不出来的。计算机辅助或全自动计算机链路分析越来越多地被应用在诸多领域，例如银行和保险机构用来进行欺诈检测，电信运营商用来分析电信网络，医疗部门用来分析流行病学和药理学，执法调查，搜索引擎用于相关性评级（反之，垃圾邮件发送者用于垃圾邮件发送，或业务负责人用来优化搜索引擎）以及进行任何有联系的对象之间的分析。
====网络鲁棒性====
====网络鲁棒性====
利用渗流理论研究了网络[34]的结构鲁棒性。当节点的一个临界比例被移除时，网络变得支离破碎。这种现象被称为渗流[35]，它代表了一种从有序-无序的临界指数的相变类型。
利用渗流理论研究了网络<ref>{{cite book |title= Complex Networks: Structure, Robustness and Function |author1=R. Cohen |author2=S. Havlin |year= 2010 |publisher= Cambridge University Press |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_com_net.php}}</ref> 的结构鲁棒性。当节点的一个临界比例被移除时，网络变得支离破碎。这种现象被称为渗流<ref>{{cite book |title= Fractals and Disordered Systems |author1=A. Bunde |author2=S. Havlin |year= 1996 |publisher= Springer |url= http://havlin.biu.ac.il/Shlomo%20Havlin%20books_fds.php}}</ref> ，它代表了一种从有序-无序的临界指数的相变类型。
====流行病分析====
====流行病分析====
SIR模型是预测全球传染病在感染人群中传播的最著名的算法之一。
SIR模型是预测全球传染病在感染人群中传播的最著名的算法之一。
: <math>S = \beta\left(\frac 1 N \right)</math>
: <math>S = \beta\left(\frac 1 N \right)</math>
这个公式描述的是人群中每个易感单元的感染“力”，其中 {{math|β}} 代表的是疾病的传播概率。
这个公式描述的是人群中每个易感单元的感染“力”，其中 {{math|β}} 代表的是疾病的传播概率。
跟踪人群中易感人数随时间的变化：
跟踪人群中易感人数随时间的变化：
: <math>\Delta S = \beta \times S {1\over N} \, \Delta t</math>
: <math>\Delta S = \beta \times S {1\over N} \, \Delta t</math>
=====从感染到康复=====
=====从感染到康复=====
: <math>\Delta I = \mu I \, \Delta t</math>
: <math>\Delta I = \mu I \, \Delta t</math>
随着时间的推移，感染人数的波动幅度为：以 <math>\mu</math> 来表示特定的恢复率，但在平均感染期 <math>{1\over \tau}</math> 内减为1，感染个体的数量为 <math>I</math>，以及时间的变化为 <math>\Delta t</math>。
随着时间的推移，感染人数的波动幅度为：以 <math>\mu</math> 来表示特定的恢复率，但在平均感染期 <math>{1\over \tau}</math> 内减为1，感染个体的数量为 <math>I</math>，以及时间的变化为 <math>\Delta t</math>。
=====感染时期=====
=====感染时期=====
就 SIR 模型而言，被流行病感染的人口数量取决于 <math>R_0</math> 的数值，或“被感染个体所感染的平均人数”。
就 SIR 模型而言，被流行病感染的人口数量取决于 <math>R_0</math> 的数值，或“被感染个体所感染的平均人数”。
: <math>R_0 = \beta\tau = {\beta\over\mu}</math>
: <math>R_0 = \beta\tau = {\beta\over\mu}</math>
====网络连接分析====
====网络连接分析====
一些网络搜索排名算法使用基于链接的中心度矩阵，包括（按顺序）Marchiori的 Hyper Search、Google 的 PageRank、Kleinberg 的 HITS 算法、CheiRank 和 TrustRank 算法。在信息科学和传播学中也进行链接分析，以便从网页的结构中理解和提取信息。例如，可以分析政客的网站或博客之间的相互联系。
一些网络搜索排名算法使用基于链接的中心度矩阵，包括（按顺序）Marchiori的 Hyper Search、Google 的 PageRank、Kleinberg 的 HITS 算法、CheiRank 和 TrustRank 算法。在信息科学和传播学中也进行链接分析，以便从网页的结构中理解和提取信息。例如，可以分析政客的网站或博客之间的相互联系。
=====PageRank=====
=====PageRank=====
PageRank算法的工作原理是随机选择“节点”或网站，然后以一定的概率“随机跳转”到其他节点。由于存在一些不容易被评估的边缘网站，因此通过随机跳转到这些其他节点，PageRank算法可以完全遍历网络。
PageRank算法的工作原理是随机选择“节点”或网站，然后以一定的概率“随机跳转”到其他节点。由于存在一些不容易被评估的边缘网站，因此通过随机跳转到这些其他节点，PageRank算法可以完全遍历网络。
每个节点 <math>x_i</math> 都有一个PageRank，定义为从 <math>j</math> 连接到 <math>i</math> 的页面总和乘以1，除以 <math>j</math> 的出度，再乘以 <math>j</math> 的“重要性”或PageRank。
每个节点 <math>x_i</math> 都有一个PageRank，定义为从 <math>j</math> 连接到 <math>i</math> 的页面总和乘以1，除以 <math>j</math> 的出度，再乘以 <math>j</math> 的“重要性”或PageRank。
: <math>x_i = \sum_{j\rightarrow i}{1\over N_j}x_j^{(k)}</math>
: <math>x_i = \sum_{j\rightarrow i}{1\over N_j}x_j^{(k)}</math>
======随机跳转======
======随机跳转======
正如上面解释的那样，PageRank通过随机跳转，尝试将PageRank分配给互联网上的每个网站。通过随机跳转可以找到一些在正常的搜索方法（如广度优先搜索和深度优先搜索）中找不到的边缘网站。
正如上面解释的那样，PageRank通过随机跳转，尝试将PageRank分配给互联网上的每个网站。通过随机跳转可以找到一些在正常的搜索方法（如广度优先搜索和深度优先搜索）中找不到的边缘网站。
在上述公式中，该算法的主要提升是添加了随机跳转，没有这些随机跳转，一些网页的PageRank可能就是0，这样是非常不好的。
在上述公式中，该算法的主要提升是添加了随机跳转，没有这些随机跳转，一些网页的PageRank可能就是0，这样是非常不好的。
第一个为 <math>\alpha</math>，代表的是随机跳转发生的概率。与此相对的是阻尼因子，即 <math>1 - \alpha</math>。
第一个为 <math>\alpha</math>，代表的是随机跳转发生的概率。与此相对的是阻尼因子，即 <math>1 - \alpha</math>。
从另一个角度来看:
: <math>R(A) = \sum {R_B\over B_\text{(outlinks)}} + \cdots + {R_n \over n_\text{(outlinks)}}</math>
===中心性度量===
===中心性度量===
关于图中节点和边的相对重要性的信息可以通过[[中心性]]度量来获得，[[中心性]]在[[社会学]]等学科广泛使用。当网络分析必须回答下面问题时，中心性度量必不可少：“为了确保消息或信息传播到网络中的全部或大部分节点，应该特别关注网络中的哪些节点？”或相反地,“应该控制哪些节点以限制疾病的传播？”。正式确立的中心性度量有[[度中心性]]、[[紧密中心性]]、[[中介中心性]]、[[本征向量中心性]]和[[Katz中心性]]。网络分析的目标通常决定了要使用的中心性度量的类型。<ref name="Wasserman_Faust"/>
关于图中节点和边的相对重要性的信息可以通过[[中心性]]度量来获得，[[中心性]]在[[社会学]]等学科广泛使用。当网络分析必须回答下面问题时，中心性度量必不可少：“为了确保消息或信息传播到网络中的全部或大部分节点，应该特别关注网络中的哪些节点？”或相反地,“应该控制哪些节点以限制疾病的传播？”。正式确立的中心性度量有[[度中心性]]、[[紧密中心性]]、[[中介中心性]]、[[本征向量中心性]]和[[Katz中心性]]。网络分析的目标通常决定了要使用的中心性度量的类型。<ref name="Wasserman_Faust"/>
*'''度中心性'''是指网络中与某节点相关联的连接（节点）数。
*'''度中心性'''是指网络中与某节点相关联的连接（节点）数。
*'''紧密中心性'''决定了网络中一个节点和其他节点的“紧密”程度，通过计算该节点和网络中其他节点之间的最短路径之和来度量。
*'''紧密中心性'''决定了网络中一个节点和其他节点的“紧密”程度，通过计算该节点和网络中其他节点之间的最短路径之和来度量。
*'''中介中心性''' 通过计算通过某节点到网络中其他节点的流量来确定该节点的相对重要性。这一点可以通过计算网络中所有节点对的路径数中包含感兴趣节点的比例来获得。组中介中心性度量通过一组节点的流量。<ref>{{cite journal | last1 = Puzis | first1 = R. | last2 = Yagil | first2 = D. | last3 = Elovici | first3 = Y. | last4 = Braha | first4 = D. | year = 2009 | title = Collaborative attack on Internet users' anonymity | url = http://necsi.edu/affiliates/braha/Internet_Research_Anonimity.pdf | journal = Internet Research | volume = 19 | issue = | page = 1 | doi = 10.1108/10662240910927821 | citeseerx = 10.1.1.219.3949 | access-date = 2015-02-08 | archive-url = https://web.archive.org/web/20131207133417/http://necsi.edu/affiliates/braha/Internet_Research_Anonimity.pdf | archive-date = 2013-12-07 | url-status = dead }}</ref>
*'''中介中心性''' 通过计算通过某节点到网络中其他节点的流量来确定该节点的相对重要性。这一点可以通过计算网络中所有节点对的路径数中包含感兴趣节点的比例来获得。组中介中心性度量通过一组节点的流量。<ref>{{cite journal | last1 = Puzis | first1 = R. | last2 = Yagil | first2 = D. | last3 = Elovici | first3 = Y. | last4 = Braha | first4 = D. | year = 2009 | title = Collaborative attack on Internet users' anonymity | url = http://necsi.edu/affiliates/braha/Internet_Research_Anonimity.pdf | journal = Internet Research | volume = 19 | issue = | page = 1 | doi = 10.1108/10662240910927821 | citeseerx = 10.1.1.219.3949 | access-date = 2015-02-08 | archive-url = https://web.archive.org/web/20131207133417/http://necsi.edu/affiliates/braha/Internet_Research_Anonimity.pdf | archive-date = 2013-12-07 | url-status = dead }}</ref>
*'''本征向量中心性''' 是度中心性的的一个更加复杂的版本，一个节点的中心性不仅依赖于它的连接数，还取决于这些连接的质量。质量因子由网络邻接矩阵的本征向量决定。
*'''本征向量中心性''' 是度中心性的的一个更加复杂的版本，一个节点的中心性不仅依赖于它的连接数，还取决于这些连接的质量。质量因子由网络邻接矩阵的本征向量决定。
*'''Katz 中心性''' 通过计算某节点与其他可到达节点间最短路径的和来度量。这些路径是有权重的，连接节点与其近邻节点的路径的权重要高于那些与距离其近邻节点较远的节点相连接的路径。
*'''Katz 中心性''' 通过计算某节点与其他可到达节点间最短路径的和来度量。这些路径是有权重的，连接节点与其近邻节点的路径的权重要高于那些与距离其近邻节点较远的节点相连接的路径。