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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.

'''<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。

'''<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。