Hong Tian and Dazi Li. Graph convolutional neural networks with am-actor-critic for minimum vertex cover problem. In 2021 33rd Chinese Control and Decision Conference (CCDC), pages 3841-3846. IEEE, 2021.
Jan Toenhoff, Martin Ritzert, Hinrikus Wolf, and Martin Grohe. Run-csp: unsupervised learning of message passing networks for binary constraint satisfaction problems. CoRR, 2019.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems (NeurIPS), pages 5998-6008, 2017.
Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. Pointer networks. NeurlPS, 28, 2015.
Haoyu Wang and Pan Li. Unsupervised learning for combinatorial optimization needs meta-learning. arXiv preprint arXiv:2301.03116, 2023.
Haoyu Peter Wang, Nan Wu, Hang Yang, Cong Hao, and Pan Li. Unsupervised learning for combinatorial optimization with principled objective relaxation. In NIPS, 2022.
Duncan J Watts and Steven H Strogatz. Collective dynamics of 'small-world' networks. nature, 393 (6684):440-442, 1998.
Nicholas C Wormald. Differential equations for random processes and random graphs. AAP, pages 1217-1235, 1995.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1):4-24, 2020.
Shunxin Xiao, Shiping Wang, Yuanfei Dai, and Wenzhong Guo. Graph neural networks in node classification: survey and evaluation. Mach. Vis. Appl., 33:1-19, 2022.
Zhihao Xing and Shikui Tu. A graph neural network assisted monte carlo tree search approach to traveling salesman problem. IEEE Access, 8:108418-108428, 2020.
Zhihao Xing, Shikui Tu, and Lei Xu. Solve traveling salesman problem by monte carlo tree search and deep neural network. arXiv preprint arXiv:2005.06879, 2020.
Ke Xu, Frédéric Boussemart, Fred Hemery, and Christophe Lecoute. Random constraint satisfaction: Easy generation of hard (satisfiable) instances. Artificial intelligence, 171(8-9):514-534, 2007.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.
Ruiyang Xu and Karl Lieberherr. Learning self-play agents for combinatorial optimization problems. KER, 35:e11, 2020.
Shenghe Xu, Shivendra S Panwar, Murali Kodialam, and TV Lakshman. Deep neural network approximated dynamic programming for combinatorial optimization. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 1684-1691, 2020.
Pinar Yanardag and S. V. N. Vishwanathan. Deep graph kernels. SIGKDD, 2015.
Feidiao Yang, Tiancheng Jin, Tie-Yan Liu, Xiaoming Sun, and Jialin Zhang. Boosting dynamic programming with neural networks for solving np-hard problems. In ACML, pages 726-739. PMLR, 2018.
Gal Yehuda, Moshe Gabel, and Assaf Schuster. It's not what machines can learn, it's what we cannot teach. In ICML, pages 10831-10841. PMLR, 2020.
Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi. Learning to dispatch for job shop scheduling via deep reinforcement learning. Advances in neural information processing systems (NeurIPS), 33:1621-1632, 2020.