Jianfei Gao and Bruno Ribeiro. On the equivalence between temporal and static equivariant graph representations. In International Conference on Machine Learning, pages 7052-7076. PMLR, 2022.
Floris Geerts and Juan L Reutter. Expressiveness and approximation properties of graph neural networks. In International Conference on Learning Representations, 2021.
Shuwen Liu, Bernardo Grau, Ian Horrocks, and Egor Kostylev. Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding. Advances in Neural Information Processing Systems, 34:2034-2045, 2021.
Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y. Hammerla. Relational graph attention networks, 2019.
Xingyue Huang, Miguel Romero Orth, Ismail Ilkan Ceylan, and Pablo Barceló. A theory of link prediction via relational weisfeiler-leman, 2023.
Haiquan Qiu, Yongqi Zhang, Yong Li, and Quanming Yao. Logical expressiveness of graph neural network for knowledge graph reasoning, 2023.
J.-Y. Cai, M. Furer, and N. Immerman. An optimal lower bound on the number of variables for graph identification. In 30th Annual Symposium on Foundations of Computer Science, pages 612-617, 1989. doi: 10.1109/SFCS.1989.63543.
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. Provably powerful graph networks, 2020.
Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. Recurrent event network: Autoregressive structure inference over temporal knowledge graphs. arXiv preprint arXiv:1904.05530, 2019.
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. Evolvegn: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5363-5370, 2020.
Youngjoo Seo, Michael Defferrard, Pierre Vandergheynst, and Xavier Bresson. Structured sequence modeling with graph convolutional recurrent networks. In International conference on neural information processing, pages 362-373. Springer, 2018.
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations, 2018.
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. Inductive representation learning on temporal graphs. In International Conference on Learning Representations, 2020.
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637, 2020a.
Petar Ristoski and Heiko Paulheim. RDF2vec: RDF graph embeddings for data mining. In International Semantic Web Conference, pages 498-514. Springer, 2016.
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna. On the equivalence between graph isomorphism testing and function approximation with gnns. Advances in neural information processing systems, 32, 2019a.
Ting Chen, Song Bian, and Yizhou Sun. Are powerful graph neural nets necessary? a dissection on graph classification. arXiv preprint arXiv:1905.04579, 2019b.
Brian A Davey and Hilary A Priestley. Introduction to lattices and order. Cambridge university press, 2002.
Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, and Lujia Pan. Predicting path failure in time-evolving graphs, 2019.