Mining most frequently changing component in evolving graphs

Yajun Yang, Jeffrey Xu Yu, Hong Gao, Jian Pei, Jianzhong Li

Abstract: Many applications see huge demands of finding important changing areas in evolving graphs. In this paper, given a series of snapshots of an evolving graph, we model and develop algorithms to capture the most frequently changing component (MFCC). Motivated by the intuition that the MFCC should capture the densest area of changes in an evolving graph, we propose a simple yet effective model. Using only one parameter, users can control tradeoffs between the “density” of the changes and the size of the detected area. We verify the effectiveness and the efficiency of our approach on real data sets systematically.