LAUSR

Abstract Community detection is an important research area for its necessity in understanding the hidden structure of complex networks. A vast variety of overlapping community detection methods have been proposed in the literature, and the local expansion method is gaining much attention for its success in dealing with large networks. However, the selection of seeds limits the quality of the identified communities for the method. The paper presents a similarity-based seeding method, in which lines and triangles are searched and selected as seeds. The proposed method selects the core of a seed by the influence of the vertices, and identifies other members of the seed by a novel function, which is a combination of vertex influence and similarity. Experimental results in synthetic networks show that the proposed seeding method outperforms other seeding methods in the state of the art. Experimental results in real-world networks with ground-truth communities show that the proposed approach outperforms other state of the art overlapping community detection algorithms, in particular, it is more than two orders of magnitude faster than the existing global algorithms with competitive quality, and it obtains much more accurate community structure than the current local algorithms without any priori information.