Spanning tree-based fast community detection methods in social networks

Abstract

In this paper, we address the problem of community detection in social networks. We present two maximum cost spanning tree-based community detection methods, namely P-SPAT and K-SPAT, for social networks. Communities are defined as dense subgraphs present in social networks. However, detecting communities in a social network can still be a challenging task, in terms of computational overheads and accuracy of the detected communities. Therefore, finding communities from a social network is considered to be an interesting problem. Again, due to practical applications of community detection techniques, it is a key area of research in social network analysis and is also well studied. Experimental results show that these methods can detect highly accurate communities faster than the state-of-the-art community detection techniques.