Uncovering fuzzy communities in networks with structural similarity.

Community detection is an important task for uncovering underlying structures and analyzing group behavior in complex networks. A fuzzy community detection method is proposed in this paper to detect fuzzy community structures without any prior knowledge. Compared with previous studies, we introduce a structural similarity to measure fuzzy relation between vertices based on local interactions between neighboring vertices. In our method, we take the fuzzy similarity between vertices and fuzzy transitivity of the similarity in network topology into consideration. Moreover, multiresolution community structures can be detected by varying the fuzzy threshold. Experimental results and comparisons with some state-of-the-art methods are presented on a variety of benchmark graphs. It shows that the method is efficient in detecting communities on both real-world and synthetic networks. [ABSTRACT FROM AUTHOR]