Overlapping Community Change-Point Detection in an Evolving Network

Change-point detection is a task that looks for specific moments across which a network changes fundamentally. Change-point detection is one of the most important challenges for overlapping community evolution analysis, and its aim is to identify the moment, type, and degree of change of a specific dynamic event when an overlapping community is evolving. In contrast to overlapping community detection, change-point detection addresses the evolution of an overlapping community rather than a network topology. In this paper, we propose such a method by reformulating an overlapping community in the form of a one-dimensional stream constrained by gentle degree fluctuation and the heterogeneous size distribution of the overlapping communities. According to the number of interacting overlapping communities involved in a specific change event, overlapping community change-points are classified as unary or binary. Based on a signal processing framework and a decision function-based strategy, our proposed method finds the change-points for both unary and binary cases. The experimental results from a synthetic dataset show that our proposed approach can ensure higher accuracy and a lower false positive rate than the traditional two-stage approach.