Fast Community Discovery and Its Evolution Tracking in Time-Evolving Social Networks

In real world, social networks are large scale, noisy and evolutionary. Communities are inherent characteristics of human interaction in social networks. Tracking evolutionary communities in dynamic social networks has become an increasingly important research topic. Several classic incremental clustering and evolutionary clustering algorithms have been proposed. But they all face a problem of controlling the balance between running time and clustering quality. In this paper, we propose a fast incremental community evolution tracking (FICET) framework to discover community and track community evolution in slowly and highly evolving networks. For higher clustering quality, this framework identifies community not only by the current network data but also by the prior community structures. For shorter running time, this framework uses subgraph-by-subgraph incremental method, and introduces core sub-graph to infer the core community. Through the introduction of core sub-graph, we can quickly capture the community evolutionary events including forming, dissolving, evolving and so on. Experiments on a series of synthetic datasets and real-world datasets demonstrate that this framework improves both the clustering quality and the time cost when compared with the state-of-the-art frameworks.