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cs.LG2025
WILTing Trees: Interpreting the Distance Between MPNN Embeddings
Masahiro Negishi, Thomas Gärtner, Pascal Welke
We investigate the distance function learned by message passing neural networks (MPNNs) in specific tasks, aiming to capture the functional distance between prediction targets that…
cs.LG2024
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
Raffaele Paolino, Sohir Maskey, Pascal Welke +1
We introduce -loopy Weisfeiler-Leman (-WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, -MPNN, that can count cycles up…
cs.LG2024
Logical Distillation of Graph Neural Networks
Alexander Pluska, Pascal Welke, Thomas Gärtner +1
We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections…