Influence of Network Structure Changes on Co-word Network Link Prediction
Chen Zhuo1,Jiang Xixi1,Zhang Xiaojuan2()
1School of Computer and Information Science, Southwest University, Chongqing 400715, China 2School of Public Administration, Sichuan University, Chengdu 610065, China
[Objective] This article studies the impacts of co-word network structure changes on link prediction using the similarity metric.[Methods] Firstly, we randomly retrieved the ISLS, LAW, BSS, COM, and Ocean literature from the core collection of Web of Science (2015 to 2020). Secondly, according to the diverse keyword frequencies, we constructed co-word networks with various topological features, such as the number of nodes and edges, the Average Clustering Coefficient, the Density, the Network Transitivity, and the Average Degree. Finally, we chose 15 traditional link prediction similarity metrics(e.g., AA, CN, RWR, and Katz) to conduct link prediction experiments on various co-word networks. [Results] We compared and analyzed the prediction effects of different similarity metrics with the network structure change. (1) In different disciplines, in most cases, the larger the overall frequency of keywords in the co-word network, the smaller the average clustering coefficient, the larger the density, network transitivity, average degree, average degree centrality, average betweenness centrality and average closeness, and the greater the possibility of poor link prediction effect. Conversely, the larger the average clustering coefficient, the smaller the other network topologies, and the better the link prediction effect. (2) Among the 15 selected similarity indicators, the RWR metric performed the best in co-word networks with different topological characteristics. The prediction performance of the Katz metrics is the most stable in different co-word networks. The prediction results of each index in the LAW discipline are most affected by the change in keyword frequency. [Limitations] Due to limited computing space, we only used one classification method and one evaluation index in this study. In addition, we did not explore some node similarity indicators (i.e., likelihood analysis-based metrics and probability model-based metrics). [Conclusions] This study provides a theoretical foundation for selecting similarity metrics of co-word networks of different disciplines.
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