Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links
Jiahua Lu, Huaxiao Liu, Shuotong Bai, Junjie Xu, Renqiang Luo, Enyan Dai

TL;DR
This paper introduces FairGuide, a framework that guides the structure of graphs towards fairness by adding new links, using a differentiable community detection task and meta-gradient strategies to improve fairness in graph neural network applications.
Contribution
The paper proposes a novel fairness-guided link addition framework for graphs, employing a differentiable community detection pseudo task and meta-gradient optimization to enhance structural fairness.
Findings
FairGuide effectively improves fairness in various graph-based tasks.
The method demonstrates strong generalizability across different datasets.
Experimental results show significant fairness enhancement without sacrificing accuracy.
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…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Ethics and Social Impacts of AI · Innovative Human-Technology Interaction
