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Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs
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 approaches ignore features in favor of structure, making it nigh-impossible to assess to what extent two graphs with close f
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