title: The reconstruction of complex networks with community structure.
abstract: Link prediction is a fundamental problem with applications in many fields ranging from biology to computer science. In the literature, most effort has been devoted to estimate the likelihood of the existence of a link between two nodes, based on observed links and nodes' attributes in a network. In this paper, we apply several representative link prediction methods to reconstruct the network, namely to add the missing links with high likelihood of existence back to the network. We find that all these existing methods fail to identify the links connecting different communities, resulting in a poor reproduction of the topological and dynamical properties of the true network. To solve this problem, we propose a community-based link prediction method. We find that our method has high prediction accuracy and is very effective in reconstructing the inter-community links.
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Futian
authors.initials: F
authors.lastname: Wang
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing 100875, P.R. China.
authors.forename: An
authors.initials: A
authors.lastname: Zeng
authors.affiliation: State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China.|School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Jinghua
authors.initials: J
authors.lastname: Xiao
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D012944:Social Support