4 papers
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…
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…
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…
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…