Bi-Lipschitz encoders in neural graph matching provide controlled GED surrogates and better alignment costs, leading to improved prediction and ranking on benchmarks.
Graph Edit Distance with General Costs Using Neural Set Divergence
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Towards Metric-Faithful Neural Graph Matching
Bi-Lipschitz encoders in neural graph matching provide controlled GED surrogates and better alignment costs, leading to improved prediction and ranking on benchmarks.