Auditing Network Embedding: An Edge Influence Based Approach

Yaojing Wang, Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu

Research output: Contribution to journalArticlepeer-review


Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work, this article aims to provide some understanding of the rationale behind the existing network embedding models, e.g., why a given embedding algorithm outputs the specific node representations and how the resulting node representations relate to the structure of the input network. In particular, we propose to discern the edge influence for two widely-studied classes of network embedding models, i.e., skip-gram based models and graph neural networks. We provide algorithms to effectively and efficiently quantify the edge influence on node representations, and further identify high-influential edges by exploiting the linkage between edge influence and network structure. Experimental evaluations are conducted on real datasets showing that: 1) in terms of quantifying edge influence, the proposed method is significantly faster (up to 2,000×2,000×) than straightforward methods with little quality loss, and 2) in terms of identifying high-influential edges, the identified edges by the proposed method have a significant impact in the context of downstream prediction task and adversarial attacking.

Original languageEnglish (US)
Pages (from-to)5211-5224
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
StatePublished - Nov 1 2022


  • Aggregates
  • Context modeling
  • Couplings
  • Edge Influence
  • Graph Neural Networks
  • Graph neural networks
  • Network Embedding
  • Network Topological Properties
  • Periodic structures
  • Prediction algorithms
  • Skip-gram Model
  • Task analysis
  • graph neural networks
  • Network embedding
  • skip-gram model
  • edge influence
  • network topological properties

ASJC Scopus subject areas

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


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