WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning

Li Liu, Penggang Chen, Xin Li*, William K. Cheung*, Youmin Zhang, Qun Liu, Guoyin Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, highly precise alignment remains challenging, especially for nodes with long-range connectivity to labeled anchors. To alleviate this limitation, we propose WL-Align which employs a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of "across-network Weisfeiler-Lehman relabeling" and "proximity-preserving representation learning". The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the "exact matching" scenario.

Original languageEnglish
Pages (from-to)445-458
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
Early online date22 May 2023
Publication statusPublished - Jan 2024

Scopus Subject Areas

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

User-Defined Keywords

  • Network alignment
  • Representation Learning
  • Social Networks
  • Weisfeiler-Lehman Test


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