# Experimental performance of graph neural networks on random instances of max-cut

@inproceedings{Yao2019ExperimentalPO, title={Experimental performance of graph neural networks on random instances of max-cut}, author={Weichi Yao and A. Bandeira and S. Villar}, booktitle={Optical Engineering + Applications}, year={2019} }

This note explores the applicability of unsupervised machine learning techniques towards hard optimization problems on random inputs. In particular we consider Graph Neural Networks (GNNs) - a class of neural networks designed to learn functions on graphs - and we apply them to the max-cut problem on random regular graphs. We focus on the max-cut problem on random regular graphs because it is a fundamental problem that has been widely studied. In particular, even though there is no known… CONTINUE READING

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