Benzi, M., Bertaccini, D., Durastante, F., and Simunec, I. (2020). "Non-Local Network Dynamics via Fractional Graph Laplacians". In: Journal of Complex Networks 8.3.
Bo, D., Wang, X., Shi, C., and Shen, H. (2021). "Beyond Low-frequency Information in Graph Convolutional Networks". In: Proceedings of the AAAI Conference on Artificial Intelligence 35.5, pp. 3950-3957.
Bodnar, C., Di Giovanni, F., Chamberlain, B., Lio, P., and Bronstein, M. (2022). "Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs". In: Advances in Neural Information Processing Systems 35, pp. 18527-18541.
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P. (2017). "Geometric Deep Learning: Going beyond Euclidean Data". In: IEEE Signal Processing Magazine 34.4, pp. 18-42.
Cai, C. and Wang, Y. (2020). A Note on Over-Smoothing for Graph Neural Networks. arXiv: 2006.13318 [cs, stat].
Chamberlain, B., Rowbottom, J., Gorinova, M. I., Bronstein, M., Webb, S., and Rossi, E. (2021). "GRAND: Graph Neural Diffusion". In: Proceedings of the 38th International Conference on Machine Learning. PMLR, pp. 1407-1418.
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., and Sun, X. (2020). "Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View". In: Proceedings of the AAAI Conference on Artificial Intelligence 34.04, pp. 3438-3445.
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y. (2020). "Simple and Deep Graph Convolutional Networks". In: Proceedings of the 37th International Conference on Machine Learning. PMLR, pp. 1725-1735.
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K. (2018). "Neural Ordinary Differential Equations". In: Advances in Neural Information Processing Systems. Vol. 31. Curran Associates, Inc.
Chien, E., Peng, J., Li, P., and Milenkovic, O. (2021). "Adaptive Universal Generalized PageRank Graph Neural Network". In: International Conference on Learning Representations.
Choi, J., Hong, S., Park, N., and Cho, S.-B. (2023). "GREAD: Graph Neural Reaction-Diffusion Networks". In: Proceedings of the 40th International Conference on Machine Learning. Vol. 202. ICML'23. Honolulu, Hawaii, USA: JMLR.org, pp. 5722-5747.
Chung, F. R. K. (1997). Spectral Graph Theory. Regional Conference Series in Mathematics no. 92. Providence, R.I: Published for the Conference Board of the mathematical sciences by the American Mathematical Society.
Defferrard, M., Bresson, X., and Vandergheynst, P. (2016). "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering". In: Advances in Neural Information Processing Systems. Vol. 29. Curran Associates, Inc.
Di Giovanni, F., Rowbottom, J., Chamberlain, B. P., Markovich, T., and Bronstein, M. M. (2023). "Understanding convolution on graphs via energies". In: Transactions on Machine Learning Research.
Du, L., Shi, X., Fu, Q., Ma, X., Liu, H., Han, S., and Zhang, D. (2022). "GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily". In: Proceedings of the ACM Web Conference 2022. WWW '22. New York, NY, USA: Association for Computing Machinery, pp. 1550-1558.
Eliasof, M., Haber, E., and Treister, E. (2021). "PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations". In: Advances in Neural Information Processing Systems. Vol. 34. Curran Associates, Inc., pp. 3836-3849.