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→网络分析 Network analysis
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>
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>
Social network analysis examines the structure of relationships between social entities.[27] These entities are often persons, but may also be groups, organizations, nation states, web sites, scholarly publications.
Since the 1970s, the empirical study of networks has played a central role in social science, and many of the mathematical and statistical tools used for studying networks have been first developed in sociology.[28] 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 diseases and health-related behaviors. It has also been applied to the study of markets, where it has been used to examine the role of trust in exchange relationships and of social mechanisms in setting prices. Similarly, it has been used to study recruitment into political movements 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 nature.[29][30]In criminology, it is being used to identify influential actors in criminal gangs, offender movements, co-offending, predict criminal activities and make policies.[31]
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、民族国家、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21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传播和与健康相关的行为。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和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，用于揭露叛乱者网络层级性和无领导性的本质。 在犯罪学中，它也被用来识别犯罪团伙、犯罪运动、共同犯罪以及预测犯罪活动等，从而制定相应的政策。 [31]
===Dynamic network analysis===
===Dynamic network analysis===
[[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.
[[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.
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.
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.
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.[7][8][9][10][32] Dynamic Network Analysis focuses on meta-networks composed of multiple types of nodes (entities) and multiple types of links. These entities can be highly varied.[7] Examples include people, organizations, topics, resources, tasks, events, locations, and beliefs.
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.
动态网络分析考察了在复杂的社会技术系统影响下，不同类别的实体之间关系的转变结构，并反映了社会稳定性以及变化，如新群体、主题和领导者的涌现。 动态网络分析集中于由多种类型的节点(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，以及检查人与想法的共同进化等方面。
===Biological network analysis===
===Biological network analysis===
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>
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>
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 motifs 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 networks has led to the development of network medicine, which looks at the effect of diseases in the interactome.[33]
随着近年来公开的生物学数据的爆炸性增长，分子网络的分析引起了人们极大的兴趣。对于生物网络的分析方法与社会网络分析分析密切相关，但通常更关注于网络中的局部模式。例如网络图是指网络中被过度表征的子图；活动图也是指类似的被过度表征的斑图，在给定网络结构下，它们是指在网络中节点和连边的贡献度被过度表示。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]
[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）motifs找到的相关资料是说：我们说motifs就是与随机网络相比出现次数较多的n-node subgraph结构。那么翻译成中文应该是什么呢？[[用户:18621066378|18621066378]]（[[用户讨论:18621066378|讨论]]）
===Link analysis===
===Link analysis===
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.
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.
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 banks and insurance agencies in fraud detection, by telecommunication operators in telecommunication network analysis, by medical sector in epidemiology and pharmacology, in law enforcement investigations, by search engines for relevance rating (and conversely by the spammers for spamdexing and by business owners for search engine optimization), and everywhere else where relationships between many objects have to be analyzed.
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、他们所拨打的电话号码和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的基于计算机的链接分析越来越多地被银行和保险机构用于欺诈检测，被电信运营商用于电信网络分析，被医疗部门用于流行病学和药理学，被用于执法调查，被用于搜索引擎用于相关性评级(反之亦然，被滥发信息者用于滥发信息，及被业务负责人优化搜索引擎) 以及被用于任何有联系对象之间的分析。
====Network robustness====
====Network robustness====
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 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 structural robustness of networks[34] 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[35] and it represents an order-disorder type of phase transition with critical exponents.
利用渗流理论研究了网络[34]的结构鲁棒性。 当节点的一个临界部分被移除时，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。
====Pandemic analysis====
====Pandemic analysis====