A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere

In three linesTopological framework for comparing trained GNNs by mapping Stochastic Block Models onto the n-dimensional sphere. Leverages compactness of graphon space, Frieze-Kannan weak regularity lemma, and Lipschitz continuity of MPNNs. Produces low-dimensional fingerprint for transfer-learning candidate retrieval without retraining.

Summary generated by Claude — human-verified