[16] F. Errica, M. Podda, D. Bacciu, and A. Micheli. A fair comparison of graph neural networks for graph classification. In International Conference on Learning Representations, 2020.
[17] M. Fey and J. E. Lenssen. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
[18] H. Gao and S. Ji. Graph u-nets. In international conference on machine learning, pages 2083-2092. PMLR, 2019.
[19] J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl. Neural message passing for quantum chemistry. In International conference on machine learning, pages 1263-1272. PMLR, 2017.
[20] D. Grattarola, D. Zambon, F. M. Bianchi, and C. Alippi. Understanding pooling in graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2022.
[21] M. Guerra, I. Spinelli, S. Scardapane, and F. M. Bianchi. Explainability in subgraphs-enhanced graph neural networks. arXiv preprint arXiv:2209.07926, 2022.
[22] A. H. Khasahmadi, K. Hassani, P. Moradi, L. Lee, and Q. Morris. Memory-based graph networks. In International Conference on Learning Representations, 2020.
[23] B. Knyazev, G. W. Taylor, and M. Amer. Understanding attention and generalization in graph neural networks. Advances in neural information processing systems, 32, 2019.
[24] J. Lee, I. Lee, and J. Kang. Self-attention graph pooling. In International conference on machine learning, pages 3734–3743. PMLR, 2019.
[25] D. Luo, W. Cheng, D. Xu, W. Yu, B. Zong, H. Chen, and X. Zhang. Parameterized explainer for graph neural network. Advances in neural information processing systems, 33:19620-19631, 2020.
[26] Z. Ma, J. Xuan, Y. G. Wang, M. Li, and P. Lio. Path integral based convolution and pooling for graph neural networks. Advances in Neural Information Processing Systems, 33:16421-16433, 2020.
[27] H. Maron, H. Ben-Hamu, H. Serviansky, and Y. Lipman. Provably powerful graph networks. Advances in neural information processing systems, 32, 2019.
[28] D. Mesquita, A. Souza, and S. Kaski. Rethinking pooling in graph neural networks. Advances in Neural Information Processing Systems, 33:2220-2231, 2020.
[29] C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe. Weisfeiler and leman go neural: Higher-order graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 4602-4609, 2019.
[30] C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663, 2020.
[31] E. Ranjan, S. Sanyal, and P. Talukdar. Asap: Adaptive structure aware pooling for learning hierarchical graph representations. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5470-5477, 2020.
[32] R. Sato. A survey on the expressive power of graph neural networks. arXiv preprint arXiv:2003.04078, 2020.
[33] R. Sato, M. Yamada, and H. Kashima. Random features strengthen graph neural networks. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pages 333-341. SIAM, 2021.
[34] N. Shervashidze, P. Schweitzer, E. J. Van Leeuwen, K. Mehlhorn, and K. M. Borgwardt. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12(9), 2011.