GNNFairViz: Visual Analysis for Graph Neural Network Fairness

Xinwu Ye, Jielin Feng, Erasmo Purificato, Ludovico Boratto, Michael Kamp, Zengfeng Huang, Siming Chen

Abstract: Recent advancements in Graph Neural Networks (GNNs) show promise for various applications like social networks and financial networks. However, they exhibit fairness issues, particularly in human-related decision contexts, risking unfair treatment of groups historically subject to discrimination. While several visual analytics studies have explored fairness in machine learning (ML), few have tackled the particular challenges posed by GNNs. We propose a visual analytics framework for GNN fairness analysis, offering insights into how attribute and structural biases may introduce model bias. Our framework is model-agnostic and tailored for real-world scenarios with multiple and multinary sensitive attributes, utilizing an extended suite of fairness metrics. To operationalize the framework, we develop GNNFairViz, a visual analysis tool that integrates seamlessly into the GNN development workflow, offering interactive visualizations. Our tool enables GNN model developers, the target users, to analyze model bias comprehensively, facilitating node selection, fairness inspection, and diagnostics. We evaluate our approach through two usage scenarios and expert interviews, confirming its effectiveness and usability in GNN fairness analysis. Furthermore, we summarize two general insights into GNN fairness based on our observations on the usage of GNNFairViz, highlighting the prevalence of the “Overwhelming Effect” in highly unbalanced datasets and the importance of suitable GNN architecture selection for bias mitigation.

External IDs:dblp:journals/tvcg/YeFPBKHC25