3 papers
cs.LG2026
Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
João Mattos, Arlei Silva
We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with…
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.LG2024
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction
João Mattos, Zexi Huang, Mert Kosan +2
Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from…