Embedding identity and interest for social networks

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

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

8 Citations (Scopus)

Abstract

Network embedding fills the gap of applying tuple-based data mining models to networked datasets through learning latent representations or embeddings. However, it may not be likely to associate latent embeddings with physical meanings just as the name, latent embedding, literally suggests. Hence, models built on embeddings may not be interpretable. In this paper, we thus propose to learn identity embeddings and interest embeddings, where user identity includes demographic and affiliation information, and interest is demonstrated by activities or topics users are interested in. With identity and interest information, we can make data mining models not only more interpretable, but also more accurate, which is demonstrated on three real-world social networks in link prediction and multi-task classification.

Original languageEnglish
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages859-860
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
CountryAustralia
CityPerth
Period3/04/177/04/17

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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