Aligning users across social networks using network embedding

Li Liu, Kwok Wai CHEUNG, Xin Li*, Lejian Liao

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

110 Citations (Scopus)

Abstract

In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with "similar" followers/followees in the embedded space. For the alignment, we add both known and potential anchor users across the networks to facilitate the transfer of context information across networks. We solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. The stochastic gradient descent and negative sampling algorithms are used to address scalability issues. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
PublisherAAAI press
Pages1774-1780
Number of pages7
ISBN (Print)9781577357704
Publication statusPublished - Jul 2016
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States, New York, United States
Duration: 9 Jul 201615 Jul 2016

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Country/TerritoryUnited States
CityNew York
Period9/07/1615/07/16

Scopus Subject Areas

  • Artificial Intelligence

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