title: Overlapping community detection via network dynamics.
abstract: The modular structure of a network is closely related to the dynamics toward clustering. In this paper, a method for community detection is proposed via the clustering dynamics of a network. The initial phases of the nodes in the network are given randomly, and then they evolve according to a set of dedicatedly designed differential equations. The phases of the nodes are naturally separated into several clusters after a period of evolution, and each cluster corresponds to a community in the network. For the networks with overlapping communities, the phases of the overlapping nodes will evolve to the interspace of the two communities. The proposed method is illustrated with applications to both synthetically generated and real-world complex networks.
authors.affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China.
authors.forename: Jianshe
authors.initials: J
authors.lastname: Wu
authors.forename: Licheng
authors.initials: L
authors.lastname: Jiao
authors.forename: Chao
authors.initials: C
authors.lastname: Jin
authors.forename: Fang
authors.initials: F
authors.lastname: Liu
authors.forename: Maoguo
authors.initials: M
authors.lastname: Gong
authors.forename: Ronghua
authors.initials: R
authors.lastname: Shang
authors.forename: Weisheng
authors.initials: W
authors.lastname: Chen