Resisting the edge-type disturbance for link prediction in heterogeneous social networks

Huan Wang, Ruigang Liu, Junyang Chen, Chuanqi Shi, Lei Fang, Shun Liu, Zhiguo Gong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-specific predictions may be contradictory for unverified edge samples with uncertain types. This challenge is termed edge-type disturbance in link prediction in heterogeneous networks. To address this challenge, we develop a disturbance-resilient prediction method (DRPM) comprising a structural characterizer, a type differentiator, and a resilient predictor. The structural characterizer is responsible for learning edge representations for link prediction. Concurrently, the type differentiator distinguishes type-specific edge representations to generate diverse type experts while maximizing their link prediction performances on specific types. Further, the resilient predictor evaluates the reliability weights of different type experts to develop a resilient prediction mechanism to aggregate discriminable predictions. Extensive experiments conducted on various real-world datasets demonstrate the importance of the explainable introduction of the edge-type disturbance and the superiority of DRPM over state-of-the-art methods.
Original languageEnglish
Article number45
Number of pages24
JournalACM Transactions on Knowledge Discovery from Data (TKDD)
Issue number2
Early online date7 Aug 2023
Publication statusPublished - 1 Feb 2024


  • Social networks
  • Data mining
  • Network sociology
  • Heterogenous network
  • Edge-type disturbance
  • Link prediction


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