Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors

Zihan Yan, Li Liu*, Xin Li, William Cheung, Youmin Zhang, Qun Liu, Guoyin Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that ‘`overly-close’' user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.

Original languageEnglish
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
Publication statusE-pub ahead of print - 12 Nov 2021

Scopus Subject Areas

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

User-Defined Keywords

  • User Alignment
  • Network Embedding
  • Pseudo Anchors
  • Meta Learning
  • Social Networks

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