A Comparison of Centrality Measures for Community Evolution Discovery

Many of real-world social networks, show structural changes over time, so they can be modeled as dynamic graphs. However, most methods in social network analysis, including community detection, are focused on performing on static networks. Therefore, methods of studying community evolution still have room for improvement. In this article, we investigated one of the methods introduced in independent community detection and matching approach. It is an approach for tracking dynamic community evolution, but it has the advantage of using methods that have been studied in detail for static networks. Previous studies have examined and compared some of the centralities that can be used in this method. In this study, we examined its performance by using other centralities called betweenness centrality and closeness centrality, and compared them with the usage of social position. Our analysis was performed on a subgraph of the word co-occurrence network, which is a type of bibliometric network, and the results of the algorithm were evaluated by experts. The results show that betweenness centrality represents more transparent and useful events and using it in community evolution discovery is recommended for small networks.