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This research proposes modifying the Laplacian operator in graph diffusion processes to mitigate bias propagation, offering theoretical insights into fairness within Graph Neural Networks. By leveraging spectral adjustments and frequency-based filtering, the method achieves improved fairness metrics with minimal computational overhead on both synthetic and real-world datasets. The work directly addresses critical ethical challenges in machine learning by providing a principled framework for fairer AI models based on graph structures.
Geometrical fairness in graph neural networks
arXiv:2606.17684v1 Announce Type: cross 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.
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.
Machine Learning (stat.ML); Computers and Society (cs.CY); Machine Learning (cs.LG)
I.2.6; G.2.2; K.4.1
arXiv:2606.17684 [stat.ML]
(or arXiv:2606.17684v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2606.17684 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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