Change-aware community detection approach for dynamic social networks

Community mining is one of the most popular issues in social network analysis. Although various changes may occur in a dynamic social network, they can be classified into two categories, gradual changes and abrupt changes. Many researchers have attempted to propose a method to discover communities in dynamic social networks with various changes more accurately. Most of them have assumed that changes in dynamic social networks occur gradually. This presumption for the dynamic social network in which abrupt changes may occur misleads the problem. Few methods have tried to detect abrupt changes, but they used the statistical approach which has such disadvantages as the need for a lot of snapshots. In this paper, we propose a novel method to detect the type of changes using the least information of social networks and then, apply it to a new community detection framework named change-aware model. The experimental results on different benchmark and real-life datasets confirmed that the new method and framework have improved the performance of community detection algorithms.