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cs.LG2026
Model-to-Data Distillation for Graph Neural Networks
Debolina Halder Lina, Arlei Silva
Graph neural networks (GNNs) increasingly rely on sophisticated architectures and training procedures to achieve desirable properties such as high predictive performance, fairness,…
cs.LG2025
Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity
João Mattos, Debolina Halder Lina, Arlei Silva
Link prediction is a fundamental task in graph machine learning with applications, ranging from social recommendation to knowledge graph completion. Fairness in this setting is cri…
cs.LG2023
Fair Graph Machine Learning under Adversarial Missingness Processes
Debolina Halder Lina, Arlei Silva
Graph Neural Networks (GNNs) have achieved state-of-the-art results in many relevant tasks where decisions might disproportionately impact specific communities. However, existing w…