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cs.LG2025
Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs
Martin Carrasco, Olga Zaghen, Kavir Sumaraj +2
A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing appr…
cs.LG2025
AdS-GNN -- a Conformally Equivariant Graph Neural Network
Maksim Zhdanov, Nabil Iqbal, Erik Bekkers +1
Conformal symmetries, i.e.\ coordinate transformations that preserve angles, play a key role in many fields, including physics, mathematics, computer vision and (geometric) machine…