“动态网络分析”的版本间的差异

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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。
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本词条由Ryan初步翻译
  
 
{{More citations needed|date=April 2009}}
 
{{More citations needed|date=April 2009}}
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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.
 
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.
  
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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。
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'''<font color="#ff8000">动态网络分析 Dynamic network analysis</font>'''是'''<font color="#ff8000">网络科学 Network Science</font>'''和'''<font color="#ff8000">网络理论 Network Theory</font>'''中将传统的'''<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>'''相结合的新兴科学领域。这个领域有两个方向。首先是动态网络数据的统计分析。第二是利用仿真来解决网络动态问题。动态网络不同于传统的社会网络，因为它们更大、更动态、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，动态网络分析考虑了社会特征的相互作用，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，这些外部因素与网络中的社会特征无关。Sampson的修道院研究是DNA使用中最著名和最早的案例之一，他在该研究中从不同间隔拍摄了同一网络的快照，并观察并分析了网络的演变。对超大型复杂网络中动态的链接利用的早期研究提供了'''<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。
  
  
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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 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
  
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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
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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
  
 
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.
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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.
  
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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。
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'''<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，一次只便于分析一种类型的链接。
  
  
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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 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.
  
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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。
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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。'''<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>'''经常被用来检查动态的社会网络(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: 一个公司的员工可以学习新的技能，增加他们对网络的价值; 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增加了网络进化的元素，并考虑了可能发生变化的环境。
  
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[[Image:DynamicNetworkAnalysisExample.jpg|right|340px|thumb|An example of a multi-entity, multi-network, dynamic network diagram]]
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[[Image:DynamicNetworkAnalysisExample.jpg|right|340px|thumb|
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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]]
  
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An example of a multi-entity, multi-network, dynamic network diagram
 
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多实体、多网络、动态网络图示例
 
  
  
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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.
  
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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。
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动态网络分析与标准社会网络分析相比，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，它们代表了链接存在的概率。
  
  
  
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==Meta-network==
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==Meta-network 元网络==
  
 
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.
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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.
  
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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。
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'''<font color="#ff8000">元网络 Meta-Network</font>'''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，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。多层级意味着某些节点可能是其他节点的成员，例如由人员和组织组成的网络，而链接之一就是谁是哪个组织的成员。
  
  
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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.
 
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.
  
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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。
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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。
  
  
  
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==Illustrative problems that people in the DNA area work on==
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==Illustrative problems that people in the DNA area work on DNA领域的人们正在研究的说明性问题==
  
  
  
 
* Developing metrics and statistics to assess and identify change within and across networks.
 
* Developing metrics and statistics to assess and identify change within and across networks.
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开发度量和统计数据，以评估和识别网络内部和网络之间的变化。
  
 
* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
 
* Developing and validating simulations to study network change, evolution, adaptation, decay. See [[Computer simulation and organizational studies]]
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开发和验证仿真以研究网络的变化，演变，适应性，衰减。
  
 
* 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 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>
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网络变化，演化，适应，衰减的开发和测试理论
  
 
* Developing and validating formal models of network generation and evolution
 
* Developing and validating formal models of network generation and evolution
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开发和验证网络生成和演化的正式模型
  
 
* Developing techniques to visualize network change overall or at the node or group level
 
* Developing techniques to visualize network change overall or at the node or group level
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开发技术以可视化整体或节点或组级别的网络变化
  
 
* 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 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
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开发统计技术，以了解随着时间的推移，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样本不同，还是因为链接和节点的底层分布随时间的变化
  
 
* Developing control processes for networks over time
 
* Developing control processes for networks over time
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在时间维度上，为网络开发控制过程
  
 
* Developing algorithms to change distributions of links in networks over time
 
* Developing algorithms to change distributions of links in networks over time
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在时间维度上，开发算法来改变网络中的链接分布
  
 
* Developing algorithms to track groups in networks over time
 
* Developing algorithms to track groups in networks over time
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在时间维度上，开发算法来跟踪网络中的群体
  
 
* Developing tools to extract or locate networks from various data sources such as texts
 
* Developing tools to extract or locate networks from various data sources such as texts
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开发从各种数据源(如文本)中提取或定位网络的工具
  
 
* Developing statistically valid measurements on networks over time
 
* Developing statistically valid measurements on networks over time
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在时间维度上，在网络上开发有效的统计度量
  
 
* Examining the robustness of network metrics under various types of missing data
 
* Examining the robustness of network metrics under various types of missing data
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检查网络指标在不同类型缺失数据下的鲁棒性
  
 
* Empirical studies of multi-mode multi-link multi-time period networks
 
* Empirical studies of multi-mode multi-link multi-time period networks
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多模式、多链路、多时段网络的实证研究
  
 
* Examining networks as probabilistic time-variant phenomena
 
* Examining networks as probabilistic time-variant phenomena
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检验网络的概率时变现象
  
 
* Forecasting change in existing networks
 
* Forecasting change in existing networks
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预测现有网络的变化
  
 
* Identifying trails through time given a sequence of networks
 
* Identifying trails through time given a sequence of networks
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在给定网络序列的时间内识别路径
  
 
* Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks
 
* Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks
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在给定的网络序列中识别节点临界性的变化，任何与多模式、多链路、多时段网络相关的东西
  
 
* Studying random walks on temporal networks<ref name="rw" />
 
* Studying random walks on temporal networks<ref name="rw" />
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研究时间网络上的随机游动
  
 
* Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes<ref name="betweenness"/>
 
* Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes<ref name="betweenness"/>
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动态网络中影响动态过程的接触序列结构特性的量化
  
 
*Assessment of covert activity<ref name="covert" /> and dark networks<ref name="dark" />
 
*Assessment of covert activity<ref name="covert" /> and dark networks<ref name="dark" />
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秘密活动的评估
  
 
*Citational analysis<ref name="citation" />
 
*Citational analysis<ref name="citation" />
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引文分析
  
 
*Social media analysis<ref name="socialmedia" />
 
*Social media analysis<ref name="socialmedia" />
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社交媒体分析
  
 
*Assessment of public health systems<ref name="health" />
 
*Assessment of public health systems<ref name="health" />
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公共卫生系统的评估
  
 
*Analysis of hospital safety outcomes<ref name="hospital" />
 
*Analysis of hospital safety outcomes<ref name="hospital" />
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医院安全结果分析
  
 
*Assessment of the structure of ethnic violence from news data<ref name="ethnic" />
 
*Assessment of the structure of ethnic violence from news data<ref name="ethnic" />
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从新闻数据中评估种族暴力的结构
  
 
*Assessment of terror groups<ref name="terror" />
 
*Assessment of terror groups<ref name="terror" />
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对恐怖组织的评估
  
 
*Online social decay of social interactions<ref name="socialdecay" />
 
*Online social decay of social interactions<ref name="socialdecay" />
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社交互动的在线社交衰退
  
 
*Visualization of large financial networks over time<ref name="dnb" />
 
*Visualization of large financial networks over time<ref name="dnb" />
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大型金融网络随时间的可视化
  
 
*Modelling of classroom interactions in schools<ref name="edu" />
 
*Modelling of classroom interactions in schools<ref name="edu" />
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学校课堂互动的建模
  
  
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* [[Graph dynamical system]]
 
* [[Graph dynamical system]]
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图动态系统
  
 
* [[International Network for Social Network Analysis]]
 
* [[International Network for Social Network Analysis]]
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国际社交网络的分析
  
 
* [[Kathleen M. Carley]]
 
* [[Kathleen M. Carley]]
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凯瑟琳·卡利
  
 
* [[Network dynamics]]
 
* [[Network dynamics]]
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网络动力学
  
 
* [[Network science]]
 
* [[Network science]]
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网络科学
  
 
* [[Sequential dynamical system]]ios13.3 deca mield(8)
 
* [[Sequential dynamical system]]ios13.3 deca mield(8)
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2020年8月29日 (六) 13:37的版本

本词条由Ryan初步翻译

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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.[1] 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.[2][3]

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是网络科学 Network Science和网络理论 Network Theory中将传统的社会网络分析 Social Network Analysis SNA、链路分析 Link Analysis LA、社会模拟 Social Simulation和多主体系统 Multi-Agent Systems MAS相结合的新兴科学领域。这个领域有两个方向。首先是动态网络数据的统计分析。第二是利用仿真来解决网络动态问题。动态网络不同于传统的社会网络，因为它们更大、更动态、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，动态网络分析考虑了社会特征的相互作用，从而调节了网络的结构和行为。动态网络分析与时间分析 Temporal Analysis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，这些外部因素与网络中的社会特征无关。Sampson的修道院研究是DNA使用中最著名和最早的案例之一，他在该研究中从不同间隔拍摄了同一网络的快照，并观察并分析了网络的演变。对超大型复杂网络中动态的链接利用的早期研究提供了动态中心性 Dynamic Centrality，动态主题 Dynamic Motifs和社交互动周期 Cycles of Social Interactions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 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

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 Meta-Network或高维网络 High-Dimensional Networks。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)[4] and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009).[5] 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 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。潜在空间模型 Latent Space Models(Sarkar 和 Moore，2005年)和基于代理的模拟 Agent-Based Simulation经常被用来检查动态的社会网络(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: 一个公司的员工可以学习新的技能，增加他们对网络的价值; 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增加了网络进化的元素，并考虑了可能发生变化的环境。

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



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社会网络分析相比，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，它们代表了链接存在的概率。


Meta-network 元网络

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 Meta-Network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，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。多层级意味着某些节点可能是其他节点的成员，例如由人员和组织组成的网络，而链接之一就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.[6] A more detailed formulation considers people, tasks, resources, knowledge, and organizations.[7] The ORA tool was developed to support meta-network analysis.[8]

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 DNA领域的人们正在研究的说明性问题

  • Developing metrics and statistics to assess and identify change within and across networks.

开发度量和统计数据，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。

  • Developing and testing theory of network change, evolution, adaptation, decay[9]

网络变化，演化，适应，衰减的开发和测试理论

  • Developing and validating formal models of network generation and evolution

开发和验证网络生成和演化的正式模型

  • Developing techniques to visualize network change overall or at the node or group level

开发技术以可视化整体或节点或组级别的网络变化

  • 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

开发统计技术，以了解随着时间的推移，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样本不同，还是因为链接和节点的底层分布随时间的变化

  • Developing control processes for networks over time

在时间维度上，为网络开发控制过程

  • Developing algorithms to change distributions of links in networks over time

在时间维度上，开发算法来改变网络中的链接分布

  • Developing algorithms to track groups in networks over time

在时间维度上，开发算法来跟踪网络中的群体

  • Developing tools to extract or locate networks from various data sources such as texts

开发从各种数据源(如文本)中提取或定位网络的工具

  • Developing statistically valid measurements on networks over time

在时间维度上，在网络上开发有效的统计度量

  • Examining the robustness of network metrics under various types of missing data

检查网络指标在不同类型缺失数据下的鲁棒性

  • Empirical studies of multi-mode multi-link multi-time period networks

多模式、多链路、多时段网络的实证研究

  • Examining networks as probabilistic time-variant phenomena

检验网络的概率时变现象

  • Forecasting change in existing networks

预测现有网络的变化

  • Identifying trails through time given a sequence of networks

在给定网络序列的时间内识别路径

  • Identifying changes in node criticality given a sequence of networks anything else related to multi-mode multi-link multi-time period networks

在给定的网络序列中识别节点临界性的变化，任何与多模式、多链路、多时段网络相关的东西

  • Studying random walks on temporal networks[10]

研究时间网络上的随机游动

  • Quantifying structural properties of contact sequences in dynamic networks, which influence dynamical processes[11]

动态网络中影响动态过程的接触序列结构特性的量化

  • Assessment of covert activity[12] and dark networks[13]

秘密活动的评估

  • Citational analysis[14]

引文分析

  • Social media analysis[15]

社交媒体分析

  • Assessment of public health systems[16]

公共卫生系统的评估

  • Analysis of hospital safety outcomes[17]

医院安全结果分析

  • Assessment of the structure of ethnic violence from news data[18]

从新闻数据中评估种族暴力的结构

  • Assessment of terror groups[19]

对恐怖组织的评估

  • Online social decay of social interactions[20]

社交互动的在线社交衰退

  • Visualization of large financial networks over time[21]

大型金融网络随时间的可视化

  • Modelling of classroom interactions in schools[22]

学校课堂互动的建模


See also

图动态系统

国际社交网络的分析

凯瑟琳·卡利

网络动力学

网络科学

顺序动力学系统


References

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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.
  • 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)
  • C. Aggarwal, K. Subbian, 2014, "Evolutionary Network Analysis: A Survey". ACM Computing Surveys, 47(1). (pdf)


External links


模板:Social networking

Category:Computer network analysis

类别: 计算机网络分析

Category:Social statistics

类别: 社会统计

Category:Methods in sociology

范畴: 社会学方法


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