Community Evolution and Change Point Detection in Time-Evolving Graphs

Abstract

How can we find communities in dynamic networks of social interactions, such as who calls whom, who emails whom, or who sells to whom? How do we store a large volume of IP network source–destination connection graphs, which grow over time? In this chapter, we study these two fundamental problems on time-evolving graphs and exploit the subtle connection between pattern mining and compression. We propose a pattern mining method, GraphScope, that automatically reveals the underlying communities in the graphs, as well as the change points in time. Our method needs no human intervention, and it is carefully designed to operate in a streaming fashion. Moreover, it is based on lossless compression principles. Therefore, in addition to revealing the fundamental structure of the graphs, the discovered patterns naturally lead to an excellent storage scheme for graph streams. Thus, our proposed GraphScope method unifies and solves both the mining and the compression problem (1) by producing meaningful time-evolving patterns agreeing with human intuition and (2) by identifying key change points in several real large time-evolving graphs. We demonstrate its efficiency and effectiveness on real data sets from several domains.