新型冠状病毒肺炎综述文献的网络结构分析

对新型冠状病毒肺炎综述文献进行网络结构分析可为新冠疫情的应对提供有效的理论支持。采用可视化软件CiteSpace对Web of Science数据库中新型冠状病毒肺炎综述文献的引文数据进行共被引网络分析，经统计发现文献共被引网络的度分布呈幂律分布，网络连通性较强且存在小世界现象。运用团渗流算法对其进行重叠社团挖掘，检测到7篇同时跨3个社团的核心文献，结合被引频次对网络重叠节点的功能进行验证。借助网络重叠节点的ID符号、介数中心性指标发现相应研究主题间联系紧密。通过不同特征的重要网络节点，能够有效挖掘到相关的研究主题。

Analysis on Network Structure of COVID-19’s Review Literature

The analysis of the network structure of COVID-19’s review literature can provide effective theoretical support for COVID-19’s response to the epidemic situation. The co-citation network analysis of COVID-19’s review literature in Web of Science database is carried out by using visualization software CiteSpace. Through statistics, it is found that the degree distribution of literature co-citation network is power-law distribution, network connectivity is strong and there is a small-world phenomenon. The clique percorlation method is used to mine the overlapping communities, and seven core literatures which span three communities at the same time are detected. Combined with the cited frequency, the function of the network overlapping nodes is verified. With the help of ID symbols and intermediate centrality index of network overlapping nodes, it is found that the corresponding research topics are closely related. Through the important network nodes with different characteristics, we can effectively mine the relevant research topics.