Schuetz, Martin J. A. and Brubaker, J. Kyle and Zhu, Zhihuai and Katzgraber, Helmut G. (2022) Graph coloring with physics-inspired graph neural networks. Physical Review Research, 4 (4). Art. No. 043131. ISSN 2643-1564. doi:10.1103/physrevresearch.4.043131. https://resolver.caltech.edu/CaltechAUTHORS:20230103-818063100.32
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Abstract
We show how graph neural networks can be used to solve the canonical graph coloring problem. We frame graph coloring as a multiclass node classification problem and utilize an unsupervised training strategy based on the statistical physics Potts model. Generalizations to other multiclass problems such as community detection, data clustering, and the minimum clique cover problem are straightforward. We provide numerical benchmark results and illustrate our approach with an end-to-end application for a real-world scheduling use case within a comprehensive encode-process-decode framework. Our optimization approach performs on par or outperforms existing solvers, with the ability to scale to problems with millions of variables.
Item Type: | Article | ||||||||
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Additional Information: | We thank M. Kastoryano, E. Kessler, T. Mullenbach, N. Pancotti, M. Resende, S. Roy, and G. Salton for fruitful discussions. | ||||||||
Group: | AWS Center for Quantum Computing | ||||||||
Issue or Number: | 4 | ||||||||
DOI: | 10.1103/physrevresearch.4.043131 | ||||||||
Record Number: | CaltechAUTHORS:20230103-818063100.32 | ||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20230103-818063100.32 | ||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||
ID Code: | 118630 | ||||||||
Collection: | CaltechAUTHORS | ||||||||
Deposited By: | Research Services Depository | ||||||||
Deposited On: | 03 Feb 2023 20:51 | ||||||||
Last Modified: | 03 Feb 2023 20:51 |
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