3 papers
cs.LG2026
From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
Debolina Halder Lina, Arlei Silva
Graph Neural Networks (GNNs) achieve high performance but can be opaque to humans, making it difficult to understand and compare the many proposed architectures. While existing exp…
cs.LG2026
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…
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…