An efficient and effective overlapping communities discovery based on agglomerative graph

Community discovery is a popular way to solve the personal service recommendation problem and has recently attracted more and more attentions of the researchers. The communities are often practically overlapping with each other, thus more and more research focus on the problem of overlapping communities detection. A common drawback of the existing algorithms to this problem is the low efficiency when dealing the large scale network. In this paper, we propose a graph compression based overlapping communities discovery algorithm, which greatly enhances the power of handling large networks even using a single computer. First, a graph compression based social network model, namely agglomerative graph, is introduced, which is a lossless compression to the original network. Then, inspired by the idea of iteration based on the selected seeds, the algorithm expands the selected seeds to the communities by optimizing the proposed community fitness function iteratively. Finally, it merges the communities of high similarity with each other to get the final results. Since the network is lossless compressed, and massive redundant computations are avoided, the results can be exactly obtained in an efficient and effective way. The experiments based on both real and synthetic datasets demonstrate efficiency and effectiveness of the proposal method in detecting overlapping communities over large scale networks.