ComFairGNN: Community Fair Graph Neural Network

TL;DR

This paper introduces ComFairGNN, a novel community-level fairness framework for GNNs that mitigates bias arising from local neighborhood diversity, improving both fairness and accuracy.

Contribution

We propose ComFairGNN, a new community-aware debiasing method that addresses limitations of existing GNN fairness evaluations and enhances bias mitigation.

Findings

Effective in reducing community-level bias

Improves fairness metrics without sacrificing accuracy

Demonstrates superiority over baseline methods on benchmark datasets

Abstract

Graph Neural Networks (GNNs) have become the leading approach for addressing graph analytical problems in various real-world scenarios. However, GNNs may produce biased predictions against certain demographic subgroups due to node attributes and neighbors surrounding a node. Most current research on GNN fairness focuses predominantly on debiasing GNNs using oversimplified fairness evaluation metrics, which can give a misleading impression of fairness. Understanding the potential evaluation paradoxes due to the complicated nature of the graph structure is crucial for developing effective GNN debiasing mechanisms. In this paper, we examine the effectiveness of current GNN debiasing methods in terms of unfairness evaluation. Specifically, we introduce a community-level strategy to measure bias in GNNs and evaluate debiasing methods at this level. Further, We introduce ComFairGNN, a novel framework designed to mitigate community-level bias in GNNs. Our approach employs a learnable coreset-based debiasing function that addresses bias arising from diverse local neighborhood distributions during GNNs neighborhood aggregation. Comprehensive evaluations on three benchmark datasets demonstrate our model's effectiveness in both accuracy and fairness metrics.