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

Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation. However, standard GNNs collapse automorphic nodes, i.e., those with identical structural roles (or orbits) into indistinguishable representations, leading to the node automor