A Fast and Accurate Algorithm for Detecting Community Structure in Social Networks

Abstract

Studies of community structure and evolution in large social networks require fast and accurate algorithms for community detection. Among the existing algorithms for community detection, the label propagation algorithm (LPA) and the Newman modularity Q algorithm (NMA) have been widely used and studied in the community detection in large social networks, since the LPA has the advantages of near-linear running time, easy implementation and without requiring parameters, and the NMA is a relatively fast algorithm and has a clear metrics to measure community structure. However, the LPA has the shortcomings that the result of the community detection is instable and has a low quality. At the same time, disadvantages of the NMA are that it bases its decisions on purely local information about individual communities and gets the local optimal solution. In this paper, combined with these two algorithms, we propose a new community detection algorithm (LP-NMA), which extends the above two algorithms (the LPA and the NMA is a special case of the new algorithm respectively). The new algorithm not only retains the advantages of these two algorithms, but also has improved the stability and quality of community detection. Experiments on real social networks have proved that this method is better than the original LPA and NMA.