On learning mixed community-specific similarity metrics for cold-start link prediction

Linchuan Xu, Xiaokai Wei, Philip S. Yu, Jiannong Cao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

We study the cold-start link prediction problem where edges between vertices is unavailable by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in many real-world social networks. Because dierent communities usually exhibit dierent intra-community homogeneities, learning a global similarity metric is not appropriate. In this paper, we thus propose to learn community-specific similarity metrics via joint community detection. Experiments on three real-world networks show that the intra-community homogeneities can be well preserved, and the mixed community-specific metrics perform better than a global similarity metric in terms of prediction accuracy.

Original languageEnglish
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages861-862
Number of pages2
ISBN (Electronic)9781450349147
DOIs
Publication statusPublished - 1 Jan 2019
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Publication series

Name26th International World Wide Web Conference 2017, WWW 2017 Companion

Conference

Conference26th International World Wide Web Conference, WWW 2017 Companion
Country/TerritoryAustralia
CityPerth
Period3/04/177/04/17

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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