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Promoting fairness in link prediction with graph enhancement.

Yezi Liu1, Hanning Chen2, Mohsen Imani2

  • 1Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States.

Frontiers in Big Data
|November 8, 2024
PubMed
Summary
This summary is machine-generated.

FairLink enhances network analysis by creating a fairness-enhanced graph, improving link prediction accuracy and fairness across sensitive groups. This scalable method is suitable for real-world applications.

Keywords:
data-centric machine learningfairnesslarge-scale graphslink predictiontrustworthy graph neural network

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Area of Science:

  • Network analysis
  • Machine learning
  • Algorithmic fairness

Background:

  • Link prediction is vital but susceptible to bias, especially concerning sensitive attributes.
  • Current debiasing methods in graph embeddings complicate training on large networks.

Purpose of the Study:

  • To develop a novel method for fair link prediction that bypasses complex debiasing during predictor training.
  • To ensure link prediction independence from sensitive node attributes.

Main Methods:

  • Propose FairLink, a method that learns a fairness-enhanced graph.
  • Maintain accuracy by mirroring the original graph's training trajectory.
  • Enhance fairness by minimizing probability differences across sensitive groups.

Main Results:

  • FairLink promotes fairness while maintaining competitive link prediction accuracy.
  • The enhanced graph demonstrates strong generalizability across various Graph Neural Network (GNN) architectures.
  • FairLink is highly scalable for large-scale real-world graph deployment.

Conclusions:

  • FairLink offers a scalable and effective solution for fair link prediction.
  • The method successfully balances fairness and accuracy in network analysis.
  • FairLink's generalizability makes it adaptable to diverse GNN models.