title: Playing the role of weak clique property in link prediction: A friend recommendation model.
abstract: An important fact in studying link prediction is that the structural properties of networks have significant impacts on the performance of algorithms. Therefore, how to improve the performance of link prediction with the aid of structural properties of networks is an essential problem. By analyzing many real networks, we find a typical structural property: nodes are preferentially linked to the nodes with the weak clique structure (abbreviated as PWCS to simplify descriptions). Based on this PWCS phenomenon, we propose a local friend recommendation (FR) index to facilitate link prediction. Our experiments show that the performance of FR index is better than some famous local similarity indices, such as Common Neighbor (CN) index, Adamic-Adar (AA) index and Resource Allocation (RA) index. We then explain why PWCS can give rise to the better performance of FR index in link prediction. Finally, a mixed friend recommendation index (labelled MFR) is proposed by utilizing the PWCS phenomenon, which further improves the accuracy of link prediction.
authors.affiliation: School of Mathematical Science, Anhui University, Hefei 230601, China.
authors.forename: Chuang
authors.initials: C
authors.lastname: Ma
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou
authors.affiliation: School of Mathematical Science, Anhui University, Hefei 230601, China.|Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, 211189, P. R. China.|Center of Information Support &Assurance Technology, Anhui University, Hefei 230601, China.
authors.forename: Hai-Feng
authors.initials: HF
authors.lastname: Zhang