Unsupervised graph neural networks match the cut quality of the Goemans-Williamson SDP on random regular graphs, while extremal optimization achieves the best cuts.
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Experimental performance of graph neural networks on random instances of max-cut
Unsupervised graph neural networks match the cut quality of the Goemans-Williamson SDP on random regular graphs, while extremal optimization achieves the best cuts.