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papersTODAY 04:00 UTC

Paper Studies Graph Matching Relaxations for Supervised Graph Prediction

A new arXiv paper examines end-to-end supervised graph prediction, where comparing predicted graphs to targets with arbitrary node orderings requires a permutation-invariant loss. The authors analyze three optimal transport-based graph matching relaxations and propose amortization to reduce the cost of these matching steps.

graph-learning/optimal-transport/arxiv/research