Disentangled link prediction for signed social networks via disentangled representation learning

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

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

3 Citations (Scopus)

Abstract

Link prediction is an important and interesting application for social networks because it can infer potential links among network participants. Existing approaches basically work with the homophily principle, i.e., people of similar characteristics tend to befriend each other. In this way, however, they are not suitable for inferring negative links or hostile links, which usually take place among people with different characteristics. Moreover, negative links tend to couple with positive links to form signed networks. In this paper, we thus study the problem of disentangled link prediction (DLP) for signed networks, which includes two separate tasks, i.e., inferring positive links and inferring negative links. Recently, representation learning methods have been proposed to solve the link prediction problem because the entire network structure can be encoded in representations. For the DLP problem, we thus propose to disentangle a node representation into two representations, and use one for positive link prediction and another for negative link prediction. Experiments on three real-world signed networks demonstrate the proposed disentangled representation learning (DRL) method significantly outperforms alternatives in the DLP problem.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages676-685
Number of pages10
ISBN (Electronic)9781509050048
DOIs
Publication statusPublished - 2 Jul 2017
Event4th International Conference on Data Science and Advanced Analytics, DSAA 2017 - Tokyo, Japan
Duration: 19 Oct 201721 Oct 2017

Publication series

NameProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
Volume2018-January

Conference

Conference4th International Conference on Data Science and Advanced Analytics, DSAA 2017
CountryJapan
CityTokyo
Period19/10/1721/10/17

Keywords

  • Link prediction
  • Representation learning
  • Signed social networks

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Computer Networks and Communications

Cite this