Geometrical fairness in graph neural networks

• URL: http://arxiv.org/abs/2606.17684v1
• Date: Tue, 16 Jun 2026 08:50:30 GMT
• Title: Geometrical fairness in graph neural networks
• Abstract summary: We introduce a fairness-aware adaptation of graph-based diffusion by modifying the underlying Laplacian operator.<n>We provide a principled analysis of the resulting behavior and establish theoretical insights into fairness properties.
• Score: 0.5872014229110214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications. Among these, recent frameworks grounded in diffusion processes provide a unifying perspective that extends traditional graph neural network formulations while addressing limitations of standard message-passing mechanisms. Despite these advances, concerns remain regarding the fairness of such models, as they may propagate or amplify biases present in the data. In this work, we introduce a fairness-aware adaptation of graph-based diffusion by modifying the underlying Laplacian operator. Our approach incorporates multiple complementary transformations, including subspace projections, spectral adjustments, and frequency-based filtering, to mitigate bias-related components. Leveraging the intrinsic smoothing properties of graph diffusion, we provide a principled analysis of the resulting behavior and establish theoretical insights into fairness properties. We evaluate the proposed framework on both synthetic and real-world datasets, demonstrating that it achieves competitive performance while improving fairness metrics with limited additional computational cost.

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