Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 4760 (2023). <\/p>\n
Article Google Scholar <\/p>\n
Schuetz, M. J. A., Brubaker, J. K. & Katzgraber, H. G. Combinatorial optimization with physics-inspired graph neural networks. Nat. Mach. Intell. 4, 367377 (2022). <\/p>\n
Article Google Scholar <\/p>\n
Cappart, Q. et al. Combinatorial optimization and reasoning with graph neural networks. J. Mach. Learn. Res. 24, 161 (2023). <\/p>\n
MathSciNet Google Scholar <\/p>\n
Khalil, E., Le Bodic, P., Song, L., Nemhauser, G. & Dilkina, B. Learning to branch in mixed integer programming. In Proc. 30th AAAI Conference on Artificial Intelligence 724731 (AAAI, 2016). <\/p>\n
Bai, Y. et al. Simgnn: a neural network approach to fast graph similarity computation. In Proc. 12th ACM International Conference on Web Search and Data Mining 384392 (ACM, 2019). <\/p>\n
Gasse, M., Chtelat, D., Ferroni, N., Charlin, L. & Lodi, A. Exact combinatorial optimization with graph convolutional neural networks. In Proc. Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 1558015592 (NeurIPS, 2019). <\/p>\n
Nair, V. et al. Solving mixed integer programs using neural networks. Preprint at https:\/\/arXiv.org\/2012.13349<\/a> (2020). <\/p>\n Li, Z., Chen, Q. & Koltun, V. Combinatorial optimization with graph convolutional networks and guided tree search. In Proc. Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al.) 537546 (NeurIPS, 2018). <\/p>\n Karalias, N. & Loukas, A. Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs. In Proc. Advances in Neural Information Processing Systems 33 (eds Larochelle, H. et al.) 66596672 (NeurIPS, 2020). <\/p>\n Toenshoff, J., Ritzert, M., Wolf, H. & Grohe, M. Graph neural networks for maximum constraint satisfaction. Front. Artif. Intell. 3, 580607 (2021). <\/p>\n Article Google Scholar <\/p>\n Mirhoseini, A. et al. A graph placement methodology for fast chip design. Nature 594, 207212 (2021). <\/p>\n Article Google Scholar <\/p>\n Yolcu, E. & Pczos, B. Learning local search heuristics for boolean satisfiability. In Proc. Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 79928003 (NeurIPS, 2019). <\/p>\n Ma, Q., Ge, S., He, D., Thaker, D. & Drori, I. Combinatorial optimization by graph pointer networks and hierarchical reinforcement learning. Preprint at https:\/\/arXiv.org\/1911.04936<\/a> (2019). <\/p>\n Kool, W., Van Hoof, H. & Welling, M. Attention, learn to solve routing problems! In International Conference on Learning Representations (ICLR, 2018). <\/p>\n Asghari, M., Fathollahi-Fard, A. M., Mirzapour Al-E-Hashem, S. M. J. & Dulebenets, M. A. Transformation and linearization techniques in optimization: a state-of-the-art survey. Mathematics 10, 283 (2022). <\/p>\n Article Google Scholar <\/p>\n Feng, S. et al. Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC Bioinformatics 22, 121 (2021). <\/p>\n Article Google Scholar <\/p>\n Murgas, K. A., Saucan, E. & Sandhu, R. Hypergraph geometry reflects higher-order dynamics in protein interaction networks. Sci. Rep. 12, 20879 (2022). <\/p>\n Article Google Scholar <\/p>\n Zhu, J., Zhu, J., Ghosh, S., Wu, W. & Yuan, J. Social influence maximization in hypergraph in social networks. IEEE Trans. Netw. Sci. Eng. 6, 801811 (2018). <\/p>\n Article MathSciNet Google Scholar <\/p>\n Xia, L., Zheng, P., Huang, X. & Liu, C. A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization. J. Intell. Manuf. 33, 22952306 (2022). <\/p>\n Article Google Scholar <\/p>\n Wen, Y., Gao, Y., Liu, S., Cheng, Q. & Ji, R. Hyperspectral image classification with hypergraph modelling. In Proc. 4th International Conference on Internet Multimedia Computing and Service 3437 (ACM, 2012). <\/p>\n Feng, Y., You, H., Zhang, Z., Ji, R. & Gao, Y. Hypergraph neural networks. In Proc. 33rd AAAI Conference on Artificial Intelligence 35583565 (AAAI, 2019). <\/p>\n Angelini, M. C. & Ricci-Tersenghi, F. Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set. Nature Mach. Intell. 5, 2931 (2023). <\/p>\n Kirkpatrick, S., Gelatt Jr, C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671680 (1983). <\/p>\n Article MathSciNet Google Scholar <\/p>\n Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https:\/\/arXiv.org\/1412.6980<\/a> (2014). <\/p>\n Benlic, U. & Hao, J.-K. Breakout local search for the max-cutproblem. Eng. Appl. Artif. Intell. 26, 11621173 (2013). <\/p>\n Article Google Scholar <\/p>\n APS dataset on Physical Review Journals, published by the American Physical Society, https:\/\/journals.aps.org\/datasets<\/a> (n.d.) <\/p>\n Ye, Y. The gset dataset, https:\/\/web.stanford.edu\/~yyye\/yyye\/Gset<\/a> (Stanford, 2003). <\/p>\n Hu, W. et al. Open graph benchmark: datasets for machine learning on graphs. In Proc. Advances in Neural Information Processing Systems 33 (eds Larochelle, H. et al.) 2211822133 (2020). <\/p>\n Ndc-substances dataset. Cornell https:\/\/www.cs.cornell.edu\/~arb\/data\/NDC-substances\/<\/a> (2018). <\/p>\n Benson, A. R., Abebe, R., Schaub, M. T., Jadbabaie, A. & Kleinberg, J. Simplicial closure and higher-order link prediction. Proc. Natl Acad. Sci. USA 115, E11221E11230 (2018). <\/p>\n Hoos, H. H., & Sttzle, T. SATLIB: An online resource for research on SAT. Sat, 2000, 283292 (2000). <\/p>\n Heydaribeni, N., Zhan, X., Zhang, R., Eliassi-Rad, T. & Koushanfar, F. Source code for Distributed constrained combinatorial optimization leveraging hypergraph neural networks. Code Ocean https:\/\/doi.org\/10.24433\/CO.4804643.v1<\/a> (2024). <\/p>\n <\/p>\n Read the original:<\/p>\n Distributed constrained combinatorial optimization leveraging hypergraph neural networks - Nature.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Wang, H. et al. Continue reading