Graph neural networks are intimately related to partial differential equations governing information diffusion on graphs. Thinking of GNNs as PDEs leads to a new broad class of graph ML methods.
K. Zhou, Q. Song, X. Huang, D. Zha, N. Zou, и X. Hu. IJCAI, стр. 1352-1358. ijcai.org, (2020)Scheduled for July 2020, Yokohama, Japan, postponed due to the Corona pandemic..
S. Wang, L. Hu, Y. Wang, X. He, Q. Sheng, M. Orgun, L. Cao, F. Ricci, и P. Yu. (2021)cite arxiv:2105.06339Comment: Accepted by IJCAI 2021 Survey Track, copyright is owned to IJCAI. The first systematic survey on graph learning based recommender systems. arXiv admin note: text overlap with arXiv:2004.11718.
M. Ryabinin, S. Popov, L. Prokhorenkova, и E. Voita. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), стр. 7317--7331. Online, Association for Computational Linguistics, (ноября 2020)
N. Shao, Y. Cui, T. Liu, S. Wang, и G. Hu. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), стр. 7187--7192. Online, Association for Computational Linguistics, (ноября 2020)
M. Peters, M. Neumann, R. Logan, R. Schwartz, V. Joshi, S. Singh, и N. Smith. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), стр. 43--54. Hong Kong, China, Association for Computational Linguistics, (ноября 2019)
G. Lee, S. Kang, и J. Whang. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, (июля 2019)