Novel heuristic density-based method for community detection in networks

Recent years have witnessed a growing recognition on the community detection in networks. Diverse techniques have been devoted to uncovering community structures in complex networks and amongst which are the density-based methods. Density-based avenues are very popular in data clustering field. They rely on two parameters which are utilized by us to process the community detection problem. In this paper, a novel view to look deep into the network structure from the community level is tested and a heuristic density-based approach for community detection is put forward. In the proposed method, firstly, both of the two parameters are under consideration and all the possible parameter pairs are exploited. These parameter pairs produce all kinds of partitions through the classic method. Secondly, these partitions are processed by our proposed strategy consisting of classification, mergence, decomposition and recombination. After employing the proposed strategy, a community division with high quality is uncovered. Experiments on both synthetic and real-world networks demonstrate the effectiveness of the proposed method.