The Gravitation-based Algorithm for Community Detecting in Large-scale Social Networks

Abstract

Communities are clusters of closely connected nodes in a social network. Detecting community structure can help us understand their network characteristics. Most popular algorithms are based on modularity optimization, such as the SLM algorithm. The SLM algorithm can detect non-overlapping communities. However, communities in real-world networks also overlap as nodes maybe belong to multiple clusters. Some models such as the BIGCLAM algorithm can be used to discover the overlapping community structure, but it has some problems in running community detection algorithms on large-scale networks. In this paper, we present a novel gravitation-based algorithm (GBA) which is inspired by the theory of galaxy evolution. The GBA algorithm is based on Newton's law of universal gravitation to simulate the process of community evolution. It includes the AGB algorithm and the AFMG algorithm. The AGB algorithm is used to detect community structure and find the center community. The AFMG algorithm is used to find the max gravity of the community. The experimental results show that our algorithm can detect overlapping communities in large-scale networks of tens millions of nodes, uncover good partitions of networks and are faster than compared methods by two to three orders of magnitude.