A detecting community method in complex networks with fuzzy clustering

Detection of community structure in complex networks is a significant aspect in social network analysis. A novel fuzzy clustering method is proposed in this paper, by which the community structure can be divided. In contrast to previous studies, the proposed method processes similarity of connecting vertices with fuzzy relation. In our method, we globally consider the fuzzy relation between vertices and the similarity in network topology to divide vertices into communities. In addition, smaller grained communities can be detected by adjusting fuzzy parameter. In order to avoid subjectivity in the selection of cluster number, a new modularity is introduced to evaluate the effectiveness of the clustering analysis. It´s proved by experiments that the method is efficient in detecting both good communities and appropriate number of clusters.