Multi-Step Community Detection and Hierarchical Time Segmentation in Evolving Networks

By Thomas Aynaud and Jean-Loup Guillaume

proceedings of the Fifth SNA-KDD Workshop Social Network Mining and Analysis, in conjunction with the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011)


Many complex systems composed of interacting objects  like social networks or the web can be modeled as graphs. They can usually be divided in dense sub-graphs with few links between them, called communities and detecting this underlying community structure may have a major impact in the understanding of these systems. We focus here on evolving graphs, for which the usual approach is to represent the state of the system at different time steps and to compute communities independently on the graph obtained at each time step. We propose in this paper to use a different framework: instead of detecting communities on each time step, we detect a unique decomposition in communities that is relevant for (almost) every time step during a given period called the time window.  We propose a definition of this new decomposition and two algorithms to detect it quickly. We validate both the approach and the algorithms on three evolving networks of different kinds showing that the quality loss at each time step is very low despite the constraint of maximization on several time steps. Since the time window length is a crucial parameter of our technique, we also propose an unsupervised hierarchical clustering algorithm to build automatically a hierarchical time segmentation into time windows. This clustering relies on a new similarity measure based on community structure. We show that it is very efficient in detecting meaningful windows.

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