An efficient algorithm for community mining with overlap in social networks

Detecting communities in social networks represents a significant task in understanding the structures and functions of networks. Several methods are developed to detect disjoint partitions. However, in real graphs vertices are often shared between communities, hence the notion of overlap. The study of this case has attracted, recently, an increasing attention and many algorithms have been designed to solve it. In this paper, we propose an overlapping communities detecting algorithm called DOCNet (Detecting overlapping communities in Networks). The main strategy of this algorithm is to find an initial core and add suitable nodes to expand it until a stopping criterion is met. Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that DOCNet is efficient and highly reliable for detecting overlapping groups, compared with four newly known proposals.