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第3行： − '''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. − − − − − − 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 − − 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. − − − − 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. 第33行：
第13行： − − − − − 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. − + − − − 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. + − 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，共同的模式却都反映了何人、何事、时间、地点、原因和方式。一个简单的元网络示例是包含有人员、任务和资源的 PCANS 公式。更详细的公式会考虑人员、任务、资源、知识和组织。ORA工具是为支持元网络分析而开发。 + + − + − − --[[用户:ZC|ZC]]([[用户讨论:ZC|讨论]]) 【审校】“该段落中多次出现“...over time”，”是否该翻译为“在时间维度上”，留有迟疑。 − + − 制定指标和统计资料，以评估和识别网络内部和网络之间的变化。 − + − 开发和验证仿真以研究网络的变化，演变，适应性，衰减。 − + − 网络变化，演化，适应，衰减的开发和测试理论 − + − 开发和验证网络成长和演化的正规模型 − + − 开发技术以将整体、节点或组级别的网络改变可视化 − + − 开发统计技术，以了解随着时间推移，在网络中观察到的差异是否仅仅是因为链接和节点分布的不同样本，还是因为链接和节点的基础分布随时间的变化 − + − 在时间维度上，为网络开发控制过程 − + − 在时间维度上，开发算法来改变网络中的链接分布 − + − 在时间维度上，开发算法来跟踪网络中的群体 − + − 开发从各种数据源资源(如文本)中提取或定位网络的工具 − + − 在时间维度上，在网络上开发有效的统计测量手段 − + − 测试网络指标在不同类型数据缺失下的稳健性 − + − 多模式、多链路、多时段网络的实证研究 − + − 检验网络的概率时变现象 − + − 预测现有网络的变化 − + − 在给定一系列网络的情况下识别经过时间的轨迹 − + − 在给定的网络序列中，识别节点临界性的变化与多模式、多链路、多时段网络相关的任何事物 − + − 研究时态网络上的随机游动 − + − 动态网络中影响动态过程的接触序列结构特性的量化 − + − 秘密活动的评估 − + − 引文分析 − + − 社交媒体分析 − + − 公共卫生系统的评估 − + − 医院安全结果分析 − + − 新闻数据中种族暴力结构的评估 − + − 恐怖组织的评估 − *Online social decay of social interactions<ref name="socialdecay" />+ − 社交互动的在线社交衰退 − + − 在时间维度上，大型金融网络可视化 − *Modelling of classroom interactions in schools<ref name="edu" /> − 学校课堂互动的建模 + − == See also == − + − 图动态系统 − * [[International Network for Social Network Analysis]] − 国际社交网络的分析 − + − 凯瑟琳·卡利 − * [[Network dynamics]] − 网络动力学 − + − − * [[Sequential dynamical system]]ios13.3 deca mield(8) − 顺序动力学系统 + − +
无编辑摘要
{{More citations needed|date=April 2009}}
{{More citations needed|date=April 2009}}
'''<font color="#ff8000">动态网络分析 Dynamic network analysis</font>'''是将传统的'''<font color="#ff8000">社会网络分析 Social Network Analysis SNA</font>'''、'''<font color="#ff8000">链路分析 Link Analysis LA</font>'''、'''<font color="#ff8000">社会模拟 Social Simulation</font>'''和'''<font color="#ff8000">多主体系统 Multi-Agent Systems MAS</font>'''与'''<font color="#ff8000">网络科学 Network Science</font>'''和'''<font color="#ff8000">网络理论 Network Theory</font>'''相结合的新兴科学领域。这个领域有两个方向。第一个是动态网络分析数据的统计分析。第二是利用仿真来解决网络动态问题。动态网络分析的网络不同于传统的社会网络，因为它们更加庞大、更加具有活力、多模式，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，动态网络分析考虑了社会特征的交互作用，从而调节了网络的结构和行为。动态网络分析与'''<font color="#ff8000">时间分析 Temporal Analysis</font>'''有关，但时间分析并不一定与动态网络分析有关，因为网络的变化有时是由外部因素造成的，这些因素与网络中的社会特征相互独立。关于使用动态网络分析中最早且最著名的案例之一，桑普森修道院的研究，他在该研究中从不同间隔拍摄了相同网络的快照，并观察和分析了网络的演变。<ref>Harrison C. White, 1992, Identity and control: A structural theory of social action. Princeton University Press.</ref>对超大规模复杂网络中链接利用动态特性的早期研究提供了'''<font color="#ff8000">动态中心性 Dynamic Centrality</font>'''，'''<font color="#ff8000">动态主题 Dynamic Motifs</font>'''和'''<font color="#ff8000">社交互动周期 Cycles of Social Interactions</font>'''的证据。<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>
'''<font color="#ff8000">动态网络分析 Dynamic network analysis</font>'''是将传统的'''<font color="#ff8000">社会网络分析 Social Network Analysis SNA</font>'''、'''<font color="#ff8000">链路分析 Link Analysis LA</font>'''、'''<font color="#ff8000">社会模拟 Social Simulation</font>'''和'''<font color="#ff8000">多主体系统 Multi-Agent Systems MAS</font>'''与'''<font color="#ff8000">网络科学 Network Science</font>'''和'''<font color="#ff8000">网络理论 Network Theory</font>'''相结合的新兴科学领域。这个领域有两个方向。第一个是动态网络分析数据的统计分析。第二是利用仿真来解决网络动态问题。动态网络分析的网络不同于传统的社会网络，因为它们更加庞大、更加具有活力、多模式，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，动态网络分析考虑了社会特征的交互作用，从而调节了网络的结构和行为。动态网络分析与'''<font color="#ff8000">时间分析 Temporal Analysis</font>'''有关，但时间分析并不一定与动态网络分析有关，因为网络的变化有时是由外部因素造成的，这些因素与网络中的社会特征相互独立。关于使用动态网络分析中最早且最著名的案例之一，桑普森修道院的研究，他在该研究中从不同间隔拍摄了相同网络的快照，并观察和分析了网络的演变。对超大规模复杂网络中链接利用动态特性的早期研究提供了'''<font color="#ff8000">动态中心性 Dynamic Centrality</font>'''，'''<font color="#ff8000">动态主题 Dynamic Motifs</font>'''和'''<font color="#ff8000">社交互动周期 Cycles of Social Interactions</font>'''的证据。
DNA统计工具通常可针对大规模网络进行优化，同时允许对多个具有多种类型的节点(多节点)和多种类型的链接(多边)的网络进行分析。多节点多重网络通常被称为
DNA统计工具通常可针对大规模网络进行优化，同时允许对多个具有多种类型的节点(多节点)和多种类型的链接(多边)的网络进行分析。多节点多重网络通常被称为
'''<font color="#ff8000">元网络 Meta-Network</font>'''或'''<font color="#ff8000">高维网络 High-Dimensional Networks</font>'''。相比之下，SNA统计工具侧重于单一模式或至多两种模式的数据，一次只能进行一种类型链接的分析。
'''<font color="#ff8000">元网络 Meta-Network</font>'''或'''<font color="#ff8000">高维网络 High-Dimensional Networks</font>'''。相比之下，SNA统计工具侧重于单一模式或至多两种模式的数据，一次只能进行一种类型链接的分析。
DNA统计工具倾向于为用户提供更多的测量手段，因为它们的测量手段同时从多个网络中提取的数据。'''<font color="#ff8000">潜在空间模型 Latent Space Models</font>'''(Sarkar 和 Moore，2005年)和'''<font color="#ff8000">基于代理的模拟 Agent-Based Simulation</font>'''经常用来测试动态的社交网络(Carley 等人，2009年)。从计算机仿真的角度来看，DNA中的节点就像量子理论中的原子一样，尽管不一定需要将节点视为概率随机的。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。特性随时间变化; 节点可以随之适应: 一个公司的职员可以学习新技能，增加他们在公司结构中的价值; 或者，抓捕了一名恐怖分子，另外三人被迫临时合作。变化从一个节点传播到下一个节点，依此类推。DNA增加了网络进化的基本要素，并考虑了可能发生变化的环境。
DNA统计工具倾向于为用户提供更多的测量手段，因为它们的测量手段同时从多个网络中提取的数据。'''<font color="#ff8000">潜在空间模型 Latent Space Models</font>'''(Sarkar 和 Moore，2005年)和'''<font color="#ff8000">基于代理的模拟 Agent-Based Simulation</font>'''经常用来测试动态的社交网络(Carley 等人，2009年)。从计算机仿真的角度来看，DNA中的节点就像量子理论中的原子一样，尽管不一定需要将节点视为概率随机的。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。特性随时间变化; 节点可以随之适应: 一个公司的职员可以学习新技能，增加他们在公司结构中的价值; 或者，抓捕了一名恐怖分子，另外三人被迫临时合作。变化从一个节点传播到下一个节点，依此类推。DNA增加了网络进化的基本要素，并考虑了可能发生变化的环境。
[[Image:DynamicNetworkAnalysisExample.jpg|right|340px|thumb|
[[Image:DynamicNetworkAnalysisExample.jpg|right|340px|thumb|
图1：An example of a multi-entity, multi-network, dynamic network diagram 多实体、多网络、动态网络图示]]
图1：An example of a multi-entity, multi-network, dynamic network diagram 多实体、多网络、动态网络图示]]
动态网络分析与标准社交网络分析相比，有三个主要特征。第一，DNA研究的不仅仅是社交网络，而是元网络。其次，基于代理建模和其他形式模拟常用于探索网络如何演变与适应，以及人为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，在许多情况中它们代表了链接存在的可能性。
动态网络分析与标准社交网络分析相比，有三个主要特征。第一，DNA研究的不仅仅是社交网络，而是元网络。其次，基于代理建模和其他形式模拟常用于探索网络如何演变与适应，以及人为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，在许多情况中它们代表了链接存在的可能性。
==元网络==
==Meta-network 元网络==
'''<font color="#ff8000">元网络 Meta-Network</font>'''是一种多模式、多链接、多层级的网络。多模式意味着有多种类型的节点; 例如，人与位置坐标的节点。多链接意味有着众多类型的链接，例如友谊和建议。多层级表明某些节点可能是其他节点的组成部分，例如由人员和组织构成的网络，并且链接之一就是谁是哪个组织的成员。
'''<font color="#ff8000">元网络 Meta-Network</font>'''是一种多模式、多链接、多层级的网络。多模式意味着有多种类型的节点; 例如，人与位置坐标的节点。多链接意味有着众多类型的链接，例如友谊和建议。多层级表明某些节点可能是其他节点的组成部分，例如由人员和组织构成的网络，并且链接之一就是谁是哪个组织的成员。
虽然不同的研究人员使用不同的模式，共同的模式却都反映了何人、何事、时间、地点、原因和方式。一个简单的元网络示例是包含有人员、任务和资源的 PCANS 公式。更详细的公式会考虑人员、任务、资源、知识和组织。ORA工具是为支持元网络分析而开发。
==DNA领域的工作者们正在研究的解释性问题==
--[[用户:ZC|ZC]]([[用户讨论:ZC|讨论]]) 【审校】“该段落中多次出现“...over time”，”是否该翻译为“在时间维度上”，留有迟疑。
* 制定指标和统计资料，以评估和识别网络内部和网络之间的变化。
* 开发和验证仿真以研究网络的变化，演变，适应性，衰减。
==Illustrative problems that people in the DNA area work on DNA领域的工作者们正在研究的解释性问题==
* 网络变化，演化，适应，衰减的开发和测试理论<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 metrics and statistics to assess and identify change within and across networks.
* 开发和验证网络成长和演化的正规模型
* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
* 开发技术以将整体、节点或组级别的网络改变可视化
* 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>
* 开发统计技术，以了解随着时间推移，在网络中观察到的差异是否仅仅是因为链接和节点分布的不同样本，还是因为链接和节点的基础分布随时间的变化
* Developing and validating formal models of network generation and evolution
* 在时间维度上，为网络开发控制过程
* Developing techniques to visualize network change overall or at the node or group level
* 在时间维度上，开发算法来改变网络中的链接分布
* 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
* 在时间维度上，开发算法来跟踪网络中的群体
* Developing control processes for networks over time
* 开发从各种数据源资源(如文本)中提取或定位网络的工具
* Developing algorithms to change distributions of links in networks over time
* 在时间维度上，在网络上开发有效的统计测量手段
* Developing algorithms to track groups in networks over time
* 测试网络指标在不同类型数据缺失下的稳健性
* Developing tools to extract or locate networks from various data sources such as texts
* 多模式、多链路、多时段网络的实证研究
* Developing statistically valid measurements on networks over time
* 检验网络的概率时变现象
* Examining the robustness of network metrics under various types of missing data
* 预测现有网络的变化
* Empirical studies of multi-mode multi-link multi-time period networks
* 在给定一系列网络的情况下识别经过时间的轨迹
* Examining networks as probabilistic time-variant phenomena
* 在给定的网络序列中，识别节点临界性的变化与多模式、多链路、多时段网络相关的任何事物
* Forecasting change in existing networks
* 研究时态网络上的随机游动
* Identifying trails through time given a sequence of networks
* 动态网络中影响动态过程的接触序列结构特性的量化
* Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks
* 秘密活动的评估
* Studying random walks on temporal networks<ref name="rw" />
* 引文分析
* Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes<ref name="betweenness"/>
* 社交媒体分析
*Assessment of covert activity<ref name="covert" /> and dark networks<ref name="dark" />
* 公共卫生系统的评估
*Citational analysis<ref name="citation" />
* 医院安全结果分析
*Social media analysis<ref name="socialmedia" />
* 新闻数据中种族暴力结构的评估
*Assessment of public health systems<ref name="health" />
* 恐怖组织的评估
*Analysis of hospital safety outcomes<ref name="hospital" />
* 社交互动的在线社交衰退
*Assessment of the structure of ethnic violence from news data<ref name="ethnic" />
* 在时间维度上，大型金融网络可视化
*Assessment of terror groups<ref name="terror" />
* 学校课堂互动的建模
== 参见 ==
*Visualization of large financial networks over time<ref name="dnb" />
* 图动态系统
* 国际社交网络的分析
* [[Graph dynamical system]]
* 凯瑟琳·卡利
* [[Kathleen M. Carley]]
* 网络动力学
* [[Network science]]
* 网络科学
网络科学
* 顺序动力学系统
==References==
==参考==
{{Reflist|refs=
{{Reflist|refs=