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Moved page from wikipedia:en:Dynamic network analysis (history)
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。
{{More citations needed|date=April 2009}}
'''Dynamic network analysis''' ('''DNA''') is an emergent scientific field that brings together traditional [[social network analysis]] (SNA), [[link analysis]] (LA), [[social simulation]] and [[multi-agent systems]] (MAS) within [[network science]] and [[network theory]]. There are two aspects of this field. The first is the [[statistical analysis]] of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of [[uncertainty]]. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network.<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref> An early study of the dynamics of link utilization in very large-scale complex networks provides evidence of dynamic centrality, dynamic motifs, and cycles of social interactions.<ref>Dan Braha, Yaneer Bar‐Yam, 2006, [https://static1.squarespace.com/static/5b68a4e4a2772c2a206180a1/t/5c5de3faf4e1fc43e7b3d21e/1549657083988/Complexity_Braha_Original_w_Cover.pdf “From centrality to temporary fame: Dynamic centrality in complex networks,”] Complexity, 12(2), 59-63.</ref><ref>Dan Braha, Yaneer Bar-Yam 2009, [https://static1.squarespace.com/static/5b68a4e4a2772c2a206180a1/t/5c5de35feb393115883fdb54/1549656928806/DN_June_12_2008_necsi_Web.pdf Time-dependent complex networks: Dynamic centrality, dynamic motifs, and cycles of social interactions.] In Adaptive Networks (pp. 39-50). Springer, Berlin, Heidelberg.</ref>
Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. There are two aspects of this field. The first is the statistical analysis of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of uncertainty. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network. An early study of the dynamics of link utilization in very large-scale complex networks provides evidence of dynamic centrality, dynamic motifs, and cycles of social interactions.
动态网络分析(Dynamic network analysis，DNA)是网络科学和网络理论中将传统的社会网络分析(social network analysis，SNA)、链接分析(link analysis，LA)、社会模拟和多智能体系统(multi-agent systems，MAS)相结合的新兴科学领域。这个领域有两个方面。首先是 DNA 数据的统计分析。第二是利用仿真来解决网络动态问题。Dna 网络不同于传统的社会网络，因为它们是更大的、动态的、多模式的、多重网络，并且可能包含不同程度的不确定性。Dna 与 SNA 的主要区别在于，DNA 考虑了社会特征之间的相互作用，制约着网络的结构和行为。脱氧核糖核酸与时间分析有关，但时间分析并不一定与脱氧核糖核酸有关，因为网络的变化有时是由外部因素造成的，这些外部因素与网络中的社会特征无关。使用 DNA 最值得注意和最早的案例之一是桑普森的修道院研究，在那里他从不同的间隔拍摄了同一网络的快照，并观察和分析了网络的演变。一项关于大规模复杂网络中链接利用动态的早期研究提供了动态中心性、动态主题和社会互动循环的证据。
DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of [[Node (networking)|nodes]] (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as
DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of nodes (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as
Dna 统计工具通常针对大规模网络进行优化，同时允许对多个网络进行分析，其中有多种类型的节点(多节点)和多种类型的链路(多丛)。多节点多重网络通常被称为
meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.
meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.
元网络或高维网络。相比之下，国民账户体系统计工具侧重于单一模式或至多两种模式的数据，一次只便于分析一种类型的链接。
DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005)<ref name="social"/> and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).<ref name="Etiology"/> From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are [[wikt:static|static]], nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.
DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005) and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009). From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are static, nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.
Dna 统计工具倾向于为用户提供更多的测量方法，因为它们可以同时使用来自多个网络的数据。潜在的空间模型(Sarkar 和 Moore，2005年)和基于代理的模拟经常被用来检查动态的社会网络(Carley 等人，2009年)。从计算机模拟的角度来看，DNA 中的节点就像量子理论中的原子---- 尽管不需要被视为概率。传统 SNA 模型中的节点是静态的，而 DNA 模型中的节点具有学习能力。财产随时间变化; 节点可以适应: 一个公司的员工可以学习新的技能，增加他们对网络的价值; 或者，抓住一个恐怖分子和三个以上的恐怖分子被迫即兴发挥。更改从一个节点传播到下一个节点，依此类推。增加了网络进化的元素，并考虑了可能发生变化的环境。
[[Image:DynamicNetworkAnalysisExample.jpg|right|340px|thumb|An example of a multi-entity, multi-network, dynamic network diagram]]
An example of a multi-entity, multi-network, dynamic network diagram
多实体、多网络、动态网络图示例
There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.
There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.
动态网络分析与标准的社会网络分析有三个主要特征。首先，DNA 研究的不仅仅是社交网络，而是元网络。其次，基于代理的建模和其他形式的模拟常用于探索网络如何演变和适应以及干预对这些网络的影响。第三，网络中的链接不是二进制的; 事实上，在许多情况下，它们代表了存在链接的概率。
==Meta-network==
A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization.
A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization.
元网络是一个多模式、多链路、多层次的网络。多模式意味着有许多类型的节点; 例如，节点、人和位置。多链接意味着有许多类型的链接，例如，友谊和建议。多层次意味着一些节点可能是其他节点的成员，例如一个由人和组织组成的网络，其中一个环节是谁是哪个组织的成员。
While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources.<ref name="pcans"/> A more detailed formulation considers people, tasks, resources, knowledge, and organizations.<ref name="smartAgents"/> The ORA tool was developed to support meta-network analysis.<ref name="toolkit" />
While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources. A more detailed formulation considers people, tasks, resources, knowledge, and organizations. The ORA tool was developed to support meta-network analysis.
虽然不同的研究人员使用不同的模式，共同的模式反映了谁，什么，什么时候，在哪里，为什么和如何。元网络的一个简单示例是包含人、任务和资源的 PCANS 公式。更详细的公式考虑人员、任务、资源、知识和组织。Ora 工具是为支持元网络分析而开发的。
==Illustrative problems that people in the DNA area work on==
* Developing metrics and statistics to assess and identify change within and across networks.
* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
* Developing and testing theory of network change, evolution, adaptation, decay<ref>{{cite journal|author=Majdandzic, A.|title=Spontaneous recovery in dynamical networks|journal=Nature Physics|volume=10|pages=34–38|year=2013|doi=10.1038/nphys2819|display-authors=etal}}</ref>
* Developing and validating formal models of network generation and evolution
* Developing techniques to visualize network change overall or at the node or group level
* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes
* Developing control processes for networks over time
* Developing algorithms to change distributions of links in networks over time
* Developing algorithms to track groups in networks over time
* Developing tools to extract or locate networks from various data sources such as texts
* Developing statistically valid measurements on networks over time
* Examining the robustness of network metrics under various types of missing data
* Empirical studies of multi-mode multi-link multi-time period networks
* Examining networks as probabilistic time-variant phenomena
* Forecasting change in existing networks
* Identifying trails through time given a sequence of networks
* Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks
* Studying random walks on temporal networks<ref name="rw" />
* Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes<ref name="betweenness"/>
*Assessment of covert activity<ref name="covert" /> and dark networks<ref name="dark" />
*Citational analysis<ref name="citation" />
*Social media analysis<ref name="socialmedia" />
*Assessment of public health systems<ref name="health" />
*Analysis of hospital safety outcomes<ref name="hospital" />
*Assessment of the structure of ethnic violence from news data<ref name="ethnic" />
*Assessment of terror groups<ref name="terror" />
*Online social decay of social interactions<ref name="socialdecay" />
*Visualization of large financial networks over time<ref name="dnb" />
*Modelling of classroom interactions in schools<ref name="edu" />
== See also ==
* [[Graph dynamical system]]
* [[International Network for Social Network Analysis]]
* [[Kathleen M. Carley]]
* [[Network dynamics]]
* [[Network science]]
* [[Sequential dynamical system]]ios13.3 deca mield(8)
==References==
{{Reflist|refs=
{{Reflist|refs=
{通货再膨胀 | 参考文献
<ref name="pcans">David Krackhardt and Kathleen M. Carley, 1998, "A PCANS Model of Structure in Organization," In proceedings of the 1998 International Symposium on Command and Control Research and Technology, Monterey, CA, June 1998, Evidence Based Research, Vienna, VA, Pp. 113-119.</ref>
<ref name="smartAgents">Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future," The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone (Eds.), Thousand Oaks, CA, Sage, Ch. 12: 206-220.</ref>
<ref name="rw">Michele Starnini, Andrea Baronchelli, Alain Barrat, 2012, Random walks on temporal networks. Phys. Rev. E 85, 056115, http://link.aps.org/doi/10.1103/PhysRevE.85.056115</ref>
<ref name="betweenness">René Pfitzner, Ingo Scholtes, Antonios Garas, Claudio Juan Tessone, Frank Schweitzer, 2012, "Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks", Physical Review Letters, Vol. 110, May 10, 2013.</ref>
<ref name="social">Purnamrita Sarkar and Andrew W. Moore. 2005. Dynamic social network analysis using latent space models. SIGKDD Explor. Newsl. 7, 2 (December 2005), 31-40.</ref>
<ref name="covert">Carley, Kathleen M., Michael K., Martin and John P. Hancock, 2009, "Dynamic Network Analysis Applied to Experiments from the Decision Architectures Research Environment," Advanced Decision Architectures for the Warfigher: Foundation and Technology, Ch. 4.</ref>
<ref name="Etiology">Kathleen M. Carley, Michael K. Martin and Brian Hirshman, 2009, "The Etiology of Social Change," Topics in Cognitive Science, 1.4:621-650</ref>
<ref name="dark">Everton, Sean, 2012, Disrupting Dark Networks, Cambridge University Press, New York, NY</ref>
<ref name="citation">Kas, Miray, Kathleen M. Carley and L. Richard Carley, 2012, "Who was Where, When? Spatiotemporal Analysis of Researcher Mobility in Nuclear Science," In proceedings of the International Workshop on Spatio Temporal data Integration and Retrieval (STIR 2012), held in conjunction
<ref name="citation">Kas, Miray, Kathleen M. Carley and L. Richard Carley, 2012, "Who was Where, When? Spatiotemporal Analysis of Researcher Mobility in Nuclear Science," In proceedings of the International Workshop on Spatio Temporal data Integration and Retrieval (STIR 2012), held in conjunction
Miray，Kathleen m. Carley and l. Richard Carley，2012，“ Who was Where，When？核科学中研究人员流动性的时空分析” ，在国际时空数据整合与检索研讨会(STIR，2012)的会议记录中，同时举行
with ICDE 2012, April 1, 2012, Washington D.C.</ref>
with ICDE 2012, April 1, 2012, Washington D.C.</ref>
2012，April 1,2012，Washington d.c. / ref
<ref name="terror">Kenney, Michael J., John Horgan, Cale Horne, Peter Vining, Kathleen M. Carley, Michael Bigrigg, [[Mia Bloom]], Kurt Braddock, 2012, Organizational adaptation in an activist network: Social networks, leadership, and change in al-Muhajiroun, Applied Ergonomics, 44(5):739-747.</ref>
<ref name="health">Merrill, Jacqueline, Mark G. Orr, Christie Y. Jeon, Rosalind V. Wilson, Jonathan Storrick and Kathleen M. Carley, 2012, "Topology of Local Health Officials’ Advice Networks: Mind the Gaps," Journal of Public Health Management Practice, 18(6): 602–608</ref>
<ref name="ethnic">Van Holt, Tracy, Jeffrey C. Johnson, Jamie Brinkley, Kathleen M. Carley and Janna Caspersen, 2012, "Structure of ethnic violence in Sudan: an automated content, meta-network and geospatial analytical approach," Computational and Mathematical Organization Theory, 18:340-355.</ref>
<ref name="hospital">Effken, Judith A.,Sheila Gephart and Kathleen M. Carley, 2013, "Using ORA to Assess the Relationship of Handoffs to Quality and Safety Outcomes," Computers, Informatics, Nursing. 31(1): 36-44.</ref>
<ref name="socialmedia">Carley, Kathleen. M., Jürgen Pfeffer, [[Huan Liu]], Fred Morstatter, Rebecca Goolsby, 2013, Near Real Time Assessment of Social Media Using Geo-Temporal Network Analytics, In Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 25–28, 2013, Niagara Falls, Canada.</ref>
<ref name="toolkit">Kathleen M. Carley. 2014. "ORA: A Toolkit for Dynamic Network Analysis and Visualization," In Reda Alhajj and Jon Rokne (Eds.) Encyclopedia of Social Network Analysis and Mining, Springer.</ref>
<ref name="socialdecay">M. Abufouda, K. A. Zweig ."A Theoretical Model for Understanding the Dynamics of Online Social Networks Decay". arXiv preprint arXiv:1610.01538.</ref>
<ref name="dnb">{{cite journal|last1=Heijmans|first1=Ronald|last2=Heuver|first2=Richard|last3=Levallois|first3=Clement|last4=van Lelyveld|first4=Iman|title=Dynamic visualization of large financial networks|journal=The Journal of Network Theory in Finance|volume=2|issue=2|year=2016|pages=57–79|issn=2055-7795|doi=10.21314/JNTF.2016.017}}</ref>
<ref name="edu">Christian Bokhove, 2016, "Exploring classroom interaction with dynamic social network analysis", International Journal of Research & Method in Education, doi:10.1080/1743727X.2016.1192116.</ref>
}}
}}
}}
== Further reading ==
*Kathleen M. Carley, 2003, "Dynamic Network Analysis" in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds.) Committee on Human Factors, National Research Council, National Research Council. Pp. 133–145, Washington, DC.
*Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future" The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone, Ch. 12, pp. 206–220, Thousand Oaks, CA, Sage.
*Kathleen M. Carley, Jana Diesner, Jeffrey Reminga, Maksim Tsvetovat, 2008, Toward an Interoperable Dynamic Network Analysis Toolkit, DSS Special Issue on Cyberinfrastructure for Homeland Security: Advances in Information Sharing, Data Mining, and Collaboration Systems. [http://www.sciencedirect.com/science/journal/01679236 Decision Support Systems] 43(4):1324-1347 ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8S-4KGG5P7-1&_user=4422&_coverDate=08%2F31%2F2007&_rdoc=20&_fmt=high&_orig=browse&_srch=doc-info(%23toc%235878%232007%23999569995%23665759%23FLA%23display%23Volume)&_cdi=5878&_sort=d&_docanchor=&_ct=52&_acct=C000059600&_version=1&_urlVersion=0&_userid=4422&md5=9459e84d7a8863039c7abd5065266250 article 20]{{dead link|date=March 2019|bot=medic}}{{cbignore|bot=medic}})
* Dan Braha and Yaneer Bar-Yam, 2006, [https://static1.squarespace.com/static/5b68a4e4a2772c2a206180a1/t/5c5def5124a694e75e6109bc/1549659985911/Theory_Time_Dependent.pdf "Time-Dependent Complex Networks."]
*Terrill L. Frantz, Kathleen M. Carley. 2009, Toward A Confidence Estimate For The Most-Central-Actor Finding. Academy of Management Annual Conference, Chicago, IL, USA, 7–11 August. (Awarded the Sage Publications/RM Division Best Student Paper Award)
*Petter Holme, Jari Saramäki, 2011, "Temporal networks". https://arxiv.org/abs/1108.1780
*C. Aggarwal, K. Subbian, 2014, "Evolutionary Network Analysis: A Survey". ACM Computing Surveys, 47(1). ([http://charuaggarwal.net/CSUR-2013-0157.pdf pdf])
== External links ==
* [http://www.eecs.harvard.edu/%7Eparkes/RadcliffeSeminar.htm Radcliffe Exploratory Seminar on Dynamic Networks]
* [http://www.casos.cs.cmu.edu/ Center for Computational Analysis of Social and Organizational Systems (CASOS)]
{{Social networking}}
{{DEFAULTSORT:Dynamic Network Analysis}}
[[Category:Computer network analysis]]
Category:Computer network analysis
类别: 计算机网络分析
[[Category:Social statistics]]
Category:Social statistics
类别: 社会统计
[[Category:Methods in sociology]]
Category:Methods in sociology
范畴: 社会学方法
<noinclude>
<small>This page was moved from [[wikipedia:en:Dynamic network analysis]]. Its edit history can be viewed at [[动态网络分析/edithistory]]</small></noinclude>
[[Category:待整理页面]]
{{More citations needed|date=April 2009}}
'''Dynamic network analysis''' ('''DNA''') is an emergent scientific field that brings together traditional [[social network analysis]] (SNA), [[link analysis]] (LA), [[social simulation]] and [[multi-agent systems]] (MAS) within [[network science]] and [[network theory]]. There are two aspects of this field. The first is the [[statistical analysis]] of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of [[uncertainty]]. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network.<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref> An early study of the dynamics of link utilization in very large-scale complex networks provides evidence of dynamic centrality, dynamic motifs, and cycles of social interactions.<ref>Dan Braha, Yaneer Bar‐Yam, 2006, [https://static1.squarespace.com/static/5b68a4e4a2772c2a206180a1/t/5c5de3faf4e1fc43e7b3d21e/1549657083988/Complexity_Braha_Original_w_Cover.pdf “From centrality to temporary fame: Dynamic centrality in complex networks,”] Complexity, 12(2), 59-63.</ref><ref>Dan Braha, Yaneer Bar-Yam 2009, [https://static1.squarespace.com/static/5b68a4e4a2772c2a206180a1/t/5c5de35feb393115883fdb54/1549656928806/DN_June_12_2008_necsi_Web.pdf Time-dependent complex networks: Dynamic centrality, dynamic motifs, and cycles of social interactions.] In Adaptive Networks (pp. 39-50). Springer, Berlin, Heidelberg.</ref>
Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. There are two aspects of this field. The first is the statistical analysis of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of uncertainty. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network. An early study of the dynamics of link utilization in very large-scale complex networks provides evidence of dynamic centrality, dynamic motifs, and cycles of social interactions.
动态网络分析(Dynamic network analysis，DNA)是网络科学和网络理论中将传统的社会网络分析(social network analysis，SNA)、链接分析(link analysis，LA)、社会模拟和多智能体系统(multi-agent systems，MAS)相结合的新兴科学领域。这个领域有两个方面。首先是 DNA 数据的统计分析。第二是利用仿真来解决网络动态问题。Dna 网络不同于传统的社会网络，因为它们是更大的、动态的、多模式的、多重网络，并且可能包含不同程度的不确定性。Dna 与 SNA 的主要区别在于，DNA 考虑了社会特征之间的相互作用，制约着网络的结构和行为。脱氧核糖核酸与时间分析有关，但时间分析并不一定与脱氧核糖核酸有关，因为网络的变化有时是由外部因素造成的，这些外部因素与网络中的社会特征无关。使用 DNA 最值得注意和最早的案例之一是桑普森的修道院研究，在那里他从不同的间隔拍摄了同一网络的快照，并观察和分析了网络的演变。一项关于大规模复杂网络中链接利用动态的早期研究提供了动态中心性、动态主题和社会互动循环的证据。
DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of [[Node (networking)|nodes]] (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as
DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of nodes (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as
Dna 统计工具通常针对大规模网络进行优化，同时允许对多个网络进行分析，其中有多种类型的节点(多节点)和多种类型的链路(多丛)。多节点多重网络通常被称为
meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.
meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time.
元网络或高维网络。相比之下，国民账户体系统计工具侧重于单一模式或至多两种模式的数据，一次只便于分析一种类型的链接。
DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005)<ref name="social"/> and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).<ref name="Etiology"/> From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are [[wikt:static|static]], nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.
DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005) and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009). From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are static, nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur.
Dna 统计工具倾向于为用户提供更多的测量方法，因为它们可以同时使用来自多个网络的数据。潜在的空间模型(Sarkar 和 Moore，2005年)和基于代理的模拟经常被用来检查动态的社会网络(Carley 等人，2009年)。从计算机模拟的角度来看，DNA 中的节点就像量子理论中的原子---- 尽管不需要被视为概率。传统 SNA 模型中的节点是静态的，而 DNA 模型中的节点具有学习能力。财产随时间变化; 节点可以适应: 一个公司的员工可以学习新的技能，增加他们对网络的价值; 或者，抓住一个恐怖分子和三个以上的恐怖分子被迫即兴发挥。更改从一个节点传播到下一个节点，依此类推。增加了网络进化的元素，并考虑了可能发生变化的环境。
[[Image:DynamicNetworkAnalysisExample.jpg|right|340px|thumb|An example of a multi-entity, multi-network, dynamic network diagram]]
An example of a multi-entity, multi-network, dynamic network diagram
多实体、多网络、动态网络图示例
There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.
There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link.
动态网络分析与标准的社会网络分析有三个主要特征。首先，DNA 研究的不仅仅是社交网络，而是元网络。其次，基于代理的建模和其他形式的模拟常用于探索网络如何演变和适应以及干预对这些网络的影响。第三，网络中的链接不是二进制的; 事实上，在许多情况下，它们代表了存在链接的概率。
==Meta-network==
A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization.
A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization.
元网络是一个多模式、多链路、多层次的网络。多模式意味着有许多类型的节点; 例如，节点、人和位置。多链接意味着有许多类型的链接，例如，友谊和建议。多层次意味着一些节点可能是其他节点的成员，例如一个由人和组织组成的网络，其中一个环节是谁是哪个组织的成员。
While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources.<ref name="pcans"/> A more detailed formulation considers people, tasks, resources, knowledge, and organizations.<ref name="smartAgents"/> The ORA tool was developed to support meta-network analysis.<ref name="toolkit" />
While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources. A more detailed formulation considers people, tasks, resources, knowledge, and organizations. The ORA tool was developed to support meta-network analysis.
虽然不同的研究人员使用不同的模式，共同的模式反映了谁，什么，什么时候，在哪里，为什么和如何。元网络的一个简单示例是包含人、任务和资源的 PCANS 公式。更详细的公式考虑人员、任务、资源、知识和组织。Ora 工具是为支持元网络分析而开发的。
==Illustrative problems that people in the DNA area work on==
* Developing metrics and statistics to assess and identify change within and across networks.
* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
* Developing and testing theory of network change, evolution, adaptation, decay<ref>{{cite journal|author=Majdandzic, A.|title=Spontaneous recovery in dynamical networks|journal=Nature Physics|volume=10|pages=34–38|year=2013|doi=10.1038/nphys2819|display-authors=etal}}</ref>
* Developing and validating formal models of network generation and evolution
* Developing techniques to visualize network change overall or at the node or group level
* Developing statistical techniques to see whether differences observed over time in networks are due to simply different samples from a distribution of links and nodes or changes over time in the underlying distribution of links and nodes
* Developing control processes for networks over time
* Developing algorithms to change distributions of links in networks over time
* Developing algorithms to track groups in networks over time
* Developing tools to extract or locate networks from various data sources such as texts
* Developing statistically valid measurements on networks over time
* Examining the robustness of network metrics under various types of missing data
* Empirical studies of multi-mode multi-link multi-time period networks
* Examining networks as probabilistic time-variant phenomena
* Forecasting change in existing networks
* Identifying trails through time given a sequence of networks
* Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks
* Studying random walks on temporal networks<ref name="rw" />
* Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes<ref name="betweenness"/>
*Assessment of covert activity<ref name="covert" /> and dark networks<ref name="dark" />
*Citational analysis<ref name="citation" />
*Social media analysis<ref name="socialmedia" />
*Assessment of public health systems<ref name="health" />
*Analysis of hospital safety outcomes<ref name="hospital" />
*Assessment of the structure of ethnic violence from news data<ref name="ethnic" />
*Assessment of terror groups<ref name="terror" />
*Online social decay of social interactions<ref name="socialdecay" />
*Visualization of large financial networks over time<ref name="dnb" />
*Modelling of classroom interactions in schools<ref name="edu" />
== See also ==
* [[Graph dynamical system]]
* [[International Network for Social Network Analysis]]
* [[Kathleen M. Carley]]
* [[Network dynamics]]
* [[Network science]]
* [[Sequential dynamical system]]ios13.3 deca mield(8)
==References==
{{Reflist|refs=
{{Reflist|refs=
{通货再膨胀 | 参考文献
<ref name="pcans">David Krackhardt and Kathleen M. Carley, 1998, "A PCANS Model of Structure in Organization," In proceedings of the 1998 International Symposium on Command and Control Research and Technology, Monterey, CA, June 1998, Evidence Based Research, Vienna, VA, Pp. 113-119.</ref>
<ref name="smartAgents">Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future," The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone (Eds.), Thousand Oaks, CA, Sage, Ch. 12: 206-220.</ref>
<ref name="rw">Michele Starnini, Andrea Baronchelli, Alain Barrat, 2012, Random walks on temporal networks. Phys. Rev. E 85, 056115, http://link.aps.org/doi/10.1103/PhysRevE.85.056115</ref>
<ref name="betweenness">René Pfitzner, Ingo Scholtes, Antonios Garas, Claudio Juan Tessone, Frank Schweitzer, 2012, "Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks", Physical Review Letters, Vol. 110, May 10, 2013.</ref>
<ref name="social">Purnamrita Sarkar and Andrew W. Moore. 2005. Dynamic social network analysis using latent space models. SIGKDD Explor. Newsl. 7, 2 (December 2005), 31-40.</ref>
<ref name="covert">Carley, Kathleen M., Michael K., Martin and John P. Hancock, 2009, "Dynamic Network Analysis Applied to Experiments from the Decision Architectures Research Environment," Advanced Decision Architectures for the Warfigher: Foundation and Technology, Ch. 4.</ref>
<ref name="Etiology">Kathleen M. Carley, Michael K. Martin and Brian Hirshman, 2009, "The Etiology of Social Change," Topics in Cognitive Science, 1.4:621-650</ref>
<ref name="dark">Everton, Sean, 2012, Disrupting Dark Networks, Cambridge University Press, New York, NY</ref>
<ref name="citation">Kas, Miray, Kathleen M. Carley and L. Richard Carley, 2012, "Who was Where, When? Spatiotemporal Analysis of Researcher Mobility in Nuclear Science," In proceedings of the International Workshop on Spatio Temporal data Integration and Retrieval (STIR 2012), held in conjunction
<ref name="citation">Kas, Miray, Kathleen M. Carley and L. Richard Carley, 2012, "Who was Where, When? Spatiotemporal Analysis of Researcher Mobility in Nuclear Science," In proceedings of the International Workshop on Spatio Temporal data Integration and Retrieval (STIR 2012), held in conjunction
Miray，Kathleen m. Carley and l. Richard Carley，2012，“ Who was Where，When？核科学中研究人员流动性的时空分析” ，在国际时空数据整合与检索研讨会(STIR，2012)的会议记录中，同时举行
with ICDE 2012, April 1, 2012, Washington D.C.</ref>
with ICDE 2012, April 1, 2012, Washington D.C.</ref>
2012，April 1,2012，Washington d.c. / ref
<ref name="terror">Kenney, Michael J., John Horgan, Cale Horne, Peter Vining, Kathleen M. Carley, Michael Bigrigg, [[Mia Bloom]], Kurt Braddock, 2012, Organizational adaptation in an activist network: Social networks, leadership, and change in al-Muhajiroun, Applied Ergonomics, 44(5):739-747.</ref>
<ref name="health">Merrill, Jacqueline, Mark G. Orr, Christie Y. Jeon, Rosalind V. Wilson, Jonathan Storrick and Kathleen M. Carley, 2012, "Topology of Local Health Officials’ Advice Networks: Mind the Gaps," Journal of Public Health Management Practice, 18(6): 602–608</ref>
<ref name="ethnic">Van Holt, Tracy, Jeffrey C. Johnson, Jamie Brinkley, Kathleen M. Carley and Janna Caspersen, 2012, "Structure of ethnic violence in Sudan: an automated content, meta-network and geospatial analytical approach," Computational and Mathematical Organization Theory, 18:340-355.</ref>
<ref name="hospital">Effken, Judith A.,Sheila Gephart and Kathleen M. Carley, 2013, "Using ORA to Assess the Relationship of Handoffs to Quality and Safety Outcomes," Computers, Informatics, Nursing. 31(1): 36-44.</ref>
<ref name="socialmedia">Carley, Kathleen. M., Jürgen Pfeffer, [[Huan Liu]], Fred Morstatter, Rebecca Goolsby, 2013, Near Real Time Assessment of Social Media Using Geo-Temporal Network Analytics, In Proceedings of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 25–28, 2013, Niagara Falls, Canada.</ref>
<ref name="toolkit">Kathleen M. Carley. 2014. "ORA: A Toolkit for Dynamic Network Analysis and Visualization," In Reda Alhajj and Jon Rokne (Eds.) Encyclopedia of Social Network Analysis and Mining, Springer.</ref>
<ref name="socialdecay">M. Abufouda, K. A. Zweig ."A Theoretical Model for Understanding the Dynamics of Online Social Networks Decay". arXiv preprint arXiv:1610.01538.</ref>
<ref name="dnb">{{cite journal|last1=Heijmans|first1=Ronald|last2=Heuver|first2=Richard|last3=Levallois|first3=Clement|last4=van Lelyveld|first4=Iman|title=Dynamic visualization of large financial networks|journal=The Journal of Network Theory in Finance|volume=2|issue=2|year=2016|pages=57–79|issn=2055-7795|doi=10.21314/JNTF.2016.017}}</ref>
<ref name="edu">Christian Bokhove, 2016, "Exploring classroom interaction with dynamic social network analysis", International Journal of Research & Method in Education, doi:10.1080/1743727X.2016.1192116.</ref>
}}
}}
}}
== Further reading ==
*Kathleen M. Carley, 2003, "Dynamic Network Analysis" in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds.) Committee on Human Factors, National Research Council, National Research Council. Pp. 133–145, Washington, DC.
*Kathleen M. Carley, 2002, "Smart Agents and Organizations of the Future" The Handbook of New Media. Edited by Leah Lievrouw and Sonia Livingstone, Ch. 12, pp. 206–220, Thousand Oaks, CA, Sage.
*Kathleen M. Carley, Jana Diesner, Jeffrey Reminga, Maksim Tsvetovat, 2008, Toward an Interoperable Dynamic Network Analysis Toolkit, DSS Special Issue on Cyberinfrastructure for Homeland Security: Advances in Information Sharing, Data Mining, and Collaboration Systems. [http://www.sciencedirect.com/science/journal/01679236 Decision Support Systems] 43(4):1324-1347 ([http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8S-4KGG5P7-1&_user=4422&_coverDate=08%2F31%2F2007&_rdoc=20&_fmt=high&_orig=browse&_srch=doc-info(%23toc%235878%232007%23999569995%23665759%23FLA%23display%23Volume)&_cdi=5878&_sort=d&_docanchor=&_ct=52&_acct=C000059600&_version=1&_urlVersion=0&_userid=4422&md5=9459e84d7a8863039c7abd5065266250 article 20]{{dead link|date=March 2019|bot=medic}}{{cbignore|bot=medic}})
* Dan Braha and Yaneer Bar-Yam, 2006, [https://static1.squarespace.com/static/5b68a4e4a2772c2a206180a1/t/5c5def5124a694e75e6109bc/1549659985911/Theory_Time_Dependent.pdf "Time-Dependent Complex Networks."]
*Terrill L. Frantz, Kathleen M. Carley. 2009, Toward A Confidence Estimate For The Most-Central-Actor Finding. Academy of Management Annual Conference, Chicago, IL, USA, 7–11 August. (Awarded the Sage Publications/RM Division Best Student Paper Award)
*Petter Holme, Jari Saramäki, 2011, "Temporal networks". https://arxiv.org/abs/1108.1780
*C. Aggarwal, K. Subbian, 2014, "Evolutionary Network Analysis: A Survey". ACM Computing Surveys, 47(1). ([http://charuaggarwal.net/CSUR-2013-0157.pdf pdf])
== External links ==
* [http://www.eecs.harvard.edu/%7Eparkes/RadcliffeSeminar.htm Radcliffe Exploratory Seminar on Dynamic Networks]
* [http://www.casos.cs.cmu.edu/ Center for Computational Analysis of Social and Organizational Systems (CASOS)]
{{Social networking}}
{{DEFAULTSORT:Dynamic Network Analysis}}
[[Category:Computer network analysis]]
Category:Computer network analysis
类别: 计算机网络分析
[[Category:Social statistics]]
Category:Social statistics
类别: 社会统计
[[Category:Methods in sociology]]
Category:Methods in sociology
范畴: 社会学方法
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