HPGRL: How Hierarchical Prototypes Make Graph Classification Resist Noise
Graph neural networks look deceptively sturdy on paper. Stack a few convolution layers, pool the node embeddings, feed the result to a classifier, and you get strong numbers on clean benchmark data. The trouble starts once the input graph stops…
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