Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links

2025-08-21

AI Summary

Graph Neural Networks (GNNs) face fairness challenges due to inherent structural biases in input graphs. To address this, we propose FairGuide—a framework that explicitly guides graph structure toward fairness via differentiable community detection to construct pseudo-downstream tasks, and employs meta-gradient optimization to identify critical edges whose addition improves structural fairness. Crucially, FairGuide models fairness as a learnable topological correction process, moving beyond post-hoc model-level debiasing. Experiments on multiple benchmark graphs demonstrate that FairGuide significantly improves both group-level (e.g., ΔSP) and individual-level (e.g., ΔEO) fairness metrics, while preserving or even enhancing performance on downstream tasks—including node classification and link prediction. These results validate the efficacy of structural debiasing for achieving fair generalization in GNNs.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Graph Neural Networks (GNNs) have achieved remarkable success across diverse applications. However, due to the biases in the graph structures, graph neural networks face significant challenges in fairness. Although the original user graph structure is generally biased, it is promising to guide these existing structures toward unbiased ones by introducing new links. The fairness guidance via new links could foster unbiased communities, thereby enhancing fairness in downstream applications. To address this issue, we propose a novel framework named FairGuide. Specifically, to ensure fairness in downstream tasks trained on fairness-guided graphs, we introduce a differentiable community detection task as a pseudo downstream task. Our theoretical analysis further demonstrates that optimizing fairness within this pseudo task effectively enhances structural fairness, promoting fairness generalization across diverse downstream applications. Moreover, FairGuide employs an effective strategy which leverages meta-gradients derived from the fairness-guidance objective to identify new links that significantly enhance structural fairness. Extensive experimental results demonstrate the effectiveness and generalizability of our proposed method across a variety of graph-based fairness tasks.

Problem

• Addressing graph neural network fairness via new link additions
• Guiding biased graph structures toward unbiased communities
• Enhancing structural fairness for diverse downstream applications

Innovation

• Introducing new links to guide graph fairness
• Using differentiable community detection as pseudo task
• Leveraging meta-gradients to identify fairness-enhancing links