Geometrical fairness in graph neural networks
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 subspa...
| Authors | Arturo Pérez-Peralta, Sandra Benítez-Peña, Blas Kolic et al. |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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