10
个编辑
更改
无编辑摘要
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.
'''<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。
'''<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 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.
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|
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。