EADP: An extended adaptive density peaks clustering for overlapping community detection in social networks

Abstract

Overlapping community detection plays an important role in studying social networks. The existing overlapping community detection methods seldom perform well on networks with complex weight distribution. Density peaks clustering (DPC) is capable of finding communities with arbitrary shape efficiently and accurately. However, DPC fails to be applied to overlapping community detection directly. In this paper, we propose an extended adaptive density peaks clustering for overlapping community detection, called EADP. To handle both weighted and unweighted social networks, EADP takes weights into consideration and incorporates a novel distance function based on common nodes to measure the distance between nodes. Moreover, unlike DPC choosing cluster centers by hand, EADP adopts a linear fitting based strategy to choose cluster centers adaptively. Experiments on real-world social networks and synthetic networks show that EADP is an effective overlapping community detection algorithm. Compared with the state-of-the-art methods, EADP performs better on those networks with complex weight distribution.