title: Detecting communities based on network topology.
abstract: Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties, and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks, and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail, and identified modules with strong functional coherence.
authors.affiliation: 1] Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China [2] Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, 90055.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.affiliation: Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, 90055.
authors.forename: Matteo
authors.initials: M
authors.lastname: Pellegrini
authors.affiliation: Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.
authors.forename: Xiaofan
authors.initials: X
authors.lastname: Wang