A Theory of AI
Graph Neural Networks
Publications
2023
B. Schäfl, L. Gruber, J. Brandstetter, and S. Hochreiter (2023) G-Signatures: Global Graph Propagation with Randomized Signatures. arXiv:2302.08811, 2023-02-17. (more) (download)
Y. Cai, Z. Zhang, P. Ghamisi, Z. Cai, X. Liu, and Y. Ding (2023) Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification. ACM Transactions on Intelligent Systems and Technology, 2023-01-09. (more) (download)
2021
Z. Zhang, Y. Cai, W. Gong, P. Ghamisi, X. Liu, and R. Gloaguen (2021) Hypergraph Convolutional Subspace Clustering with Multihop Aggregation for Hyperspectral Image. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 676-686, 2021-12-21. (more) (download)
F. Kratzert, D. Klotz, M. Gauch, C. Klingler, G. Nearing, and S. Hochreiter (2021) Large-Scale River Network Modeling Using Graph Neural Networks. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13375, 2021-03-03. (more) (download)
2020
H. Martin, D. Bucher, Y. Hong, R. Buffat, C. Rupprecht, and M. Raubal (2020) Graph-ResNets for short-term traffic forecasts in almost unknown cities. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123, 153-163, 2020-08-19. (more) (download)