Structural representation learning for user alignment across social networks

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Aligning users across different social networks has become increasingly studied as an important task to social network analysis. In this paper, we propose a novel representation learning method that mainly exploits social structures for the network alignment. In particular, the proposed network embedding framework models the follower-ship and followee-ship of each user explicitly as input and output context vectors, while preserving the proximity of users with 'similar' followers and followees in the embedded space. We incorporate both known and predicted user anchors across the networks as constraints to facilitate the transfer of context information to achieve accurate user alignment. Both network embedding and user alignment are inferred under a unified optimization framework with negative sampling adopted to ensure scalability. Also, variants of the proposed framework, including the incorporation of higher-order structural features, are also explored for further boosting the alignment accuracy. Extensive experiments on large-scale social and academia network datasets demonstrate the efficacy of our proposed model compared with state-of-the-art methods.

Original languageEnglish
Article number8691782
Pages (from-to)1824-1837
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number9
DOIs
Publication statusPublished - 1 Sep 2020

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • network embedding
  • representation learning
  • social networks
  • User alignment

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