title: Weight prediction in complex networks based on neighbor set.
abstract: Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.
authors.affiliation: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Boyao
authors.initials: B
authors.lastname: Zhu
authors.affiliation: College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Yongxiang
authors.initials: Y
authors.lastname: Xia
authors.affiliation: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
authors.forename: Xue-Jun
authors.initials: XJ
authors.lastname: Zhang