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Télécom Paris, Institut Polytechnique de Paris
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Edge-girth outperforms traditional structural descriptors in GNNs, achieving a significant reduction in error while exposing inherent limitations in graph discrimination.
Link prediction models amplify structural biases beyond immediate connections, but a new $k$-hop fairness approach can mitigate these disparities with favorable performance trade-offs.