Link prediction based on linear dynamical response

Hua Gao, Jianbin Huang, Qiang Cheng, Heli Sun, Baoli Wang, He Li

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Link prediction has attracted increasing research attention recently, which aims to predict missing links in complex networks. However, the existing link prediction methods are primarily based on network structures alone, which are incapable of capturing the dynamics defined on top of the fixed network structures. In this paper, we introduce a linear dynamical response-based similarity measure between nodes into link prediction task. To address the efficiency problem, we design a new iterative procedure to avoid the explicit computation of linear dynamical response (LDR)index. Empirically, we conduct extensive experiments on real networks from various fields. The results show that LDR index leads to promising predicting performance for link prediction.

Original languageEnglish
Article number121397
JournalPhysica A: Statistical Mechanics and its Applications
Volume527
DOIs
StatePublished - Aug 1 2019

Bibliographical note

Publisher Copyright:
© 2019

Keywords

  • Complex networks
  • Linear dynamical response
  • Link prediction

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability

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