Finding Hamiltonian Cycles with Graph Neural Networks

We train a small message-passing graph neural network to predict Hamiltonian cycles on Erdos-Renyl random graphs in a critical regime. It outperforms existing hand-crafted heuristics after about 2.5 hours of training on a single GPU. Our findings encourage an alternative approach to solving computat...

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Vydané v:	2023 International Symposium on Image and Signal Processing and Analysis (ISPA) s. 1 - 6
Hlavní autori:	Bosnic, Filip, Sikic, Mile
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 18.09.2023
Predmet:	Graph algorithms Graphics processing units Heuristics design Machine learning Machine learning algorithms Neural nets NP-hard problem Signal processing Signal processing algorithms Supervised learning Training
ISSN:	1849-2266
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