Path-based estimation for link prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Guoshuai Ma
  • Hongren Yan
  • Yuhua Qian
  • Lingfeng Wang
  • Zhongying Zhao

Detail(s)

Original languageEnglish
Pages (from-to)2443-2458
Journal / PublicationInternational Journal of Machine Learning and Cybernetics
Volume12
Issue number9
Online published1 Apr 2021
Publication statusPublished - Sep 2021

Abstract

Link prediction has received a great deal of attention from researchers. Most of the existing researches are based on the network topology but ignore the importance of its preference; for aggregating multiple pieces of information, they normally sum up them directly. In this paper, a path-based probabilistic model is proposed to estimate the potential connectivity between any two nodes. It takes carefully the effective influence of nodes and the dependency among paths between two fixed nodes into account. Furthermore, we formulate the connectivity of two inner-community nodes and that of two inter-community nodes. The qualitative analysis shows that the links between inner-community nodes are more likely to be predicted by the proposed model. The performance is verified on both the multi-barbell network and Lesmis network. Considering the proposed model’s practicability, we develop an algorithm that iterates over the adjacent matrix to simulate paths of different lengths, with the parameters automatically grid-searched. The results of the experiments show that the proposed model outperforms competitive methods.

Research Area(s)

  • Community structure, Link prediction, Preferential attachment

Citation Format(s)

Path-based estimation for link prediction. / Ma, Guoshuai; Yan, Hongren; Qian, Yuhua; Wang, Lingfeng; Dang, Chuangyin; Zhao, Zhongying.

In: International Journal of Machine Learning and Cybernetics, Vol. 12, No. 9, 09.2021, p. 2443-2458.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review