Change point detection in temporal networks based on graph snapshot similarity measures

This paper addresses the challenge of change point detection in temporal networks, a critical task across various domains, including life sciences and socioeconomic activities. Continuous analysis and problem-solving within dynamic networks are essential in these fields. While much attention has been given to binary cases, this study extends the scope to include change point detection in weighted networks, an important dimension of edge analysis in dynamic networks. We introduce a novel distance metric called the Interval Sum Absolute Difference Distance (ISADD) to measure the distance between two graph snapshots. Additionally, we apply a Gaussian radial basis function to transform this distance into a similarity score between graph snapshots. This similarity score function effectively identifies individual change points. Furthermore, we employ a bisection detection algorithm to extend the method to detect multiple change points. Experimental results on both simulated and real-world data demonstrate the efficacy of the proposed framework.