[30] Pál András Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer. DropGNN: Random dropouts increase the expressiveness of graph neural networks. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=fpQojkIV5q8.
[31] Leonardo Cotta, Christopher Morris, and Bruno Ribeiro. Reconstruction for powerful graph representations. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=ZKbZ4mebI91.
[32] Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. Labeling trick: A theory of using graph neural networks for multi-node representation learning. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021. URL https://openreview.net/forum?id=Hcr9mgBG6ds.
[33] Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. In Advances in Neural Information Processing Systems, pages 5165-5175, 2018.
[34] Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, and Haggai Maron. Equivariant subgraph aggregation networks. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=dFbKQaRk15w.
[35] Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. Invariant and equivariant graph networks. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=Syx72jC9tm.
[36] Waiss Azizian and marc lelage. Expressive power of invariant and equivariant graph neural networks. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=1xHgXYN4bw1.
[37] Floris Geerts. The expressive power of kth-order invariant graph networks. ArXiv, abs/2007.12035, 2020.
[38] Ralph Abboud, Ismail Ilkan Ceylan, Martin Grohe, and Thomas Lukasiewicz. The surprising power of graph neural networks with random node initialization. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI), 2021.
[39] Ryoma Sato, Makoto Yamada, and Hisashi Kashima. Random features strengthen graph neural networks. In Proceedings of the 2021 SIAM International Conference on Data Mining, SDM, 2021.
[40] Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M. Bronstein. Improving graph neural network expressivity via subgraph isomorphism counting, 2021. URL https://openreview.net/forum?id=LTOKSFnQDWF.
[41] Bohang Zhang, Shengjie Luo, Liwei Wang, and Di He. Rethinking the expressive power of GNNs via graph biconnectivity. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=r9hNv76KoT3.
[42] Zuoyu Yan, Junru Zhou, Liangcai Gao, Zhi Tang, and Muhan Zhang. Efficiently counting substructures by subgraph gnns without running gnn on subgraphs, 2023.
[43] Omri Puny, Derek Lim, Bobak T. Kiani, Haggai Maron, and Yaron Lipman. Equivariant polynomials for graph neural networks, 2023.
[44] Muhammet Balcilar, Pierre Héroux, Benoit Gauzère, Pascal Vasseur, Sébastien Adam, and Paul Honeine. Breaking the limits of message passing graph neural networks. In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
[45] Yanbo Wang and Muhan Zhang. Towards better evaluation of gnn expressiveness with brec dataset, 2023.