title: Enhanced community structure detection in complex networks with partial background information.
abstract: Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework.
authors.affiliation: School of Statistics and Mathematics, Central University of Finance and Economics, P.R.China.
authors.forename: Zhong-Yuan
authors.initials: ZY
authors.lastname: Zhang
authors.forename: Kai-Di
authors.initials: KD
authors.lastname: Sun
authors.forename: Si-Qi
authors.initials: SQ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D019058:Community Networks; D053858:Metabolic Networks and Pathways; D008962:Models, Theoretical; D012111:Residence Characteristics