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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
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…