aiqbench analysis
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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