Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction

Chen Shao, Donald Loveland, Tobias Käfer +1

Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation. However, standard GNNs collapse automorphic nodes, i.e…