[6] Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael Bronstein. Latentgraph learning for disease prediction. In Medical Image Computing and Computer Assisted Intervention, pages 643-653, 2020.
[7] Haitz Sáez de Ocariz Borde, Anees Kazi, Federico Barbero, and Pietro Lio. Latent graph inference using product manifolds. In The Eleventh International Conference on Learning Representations, 2023.
[8] Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli. A fair comparison of graph neural networks for graph classification. In International Conference on Learning Representations, 2020.
[9] Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, and Bryan Perozzi. Ugsl: A unified framework for benchmarking graph structure learning. In the 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at the 40 th International Conference on Machine Learning, 2023.
[10] Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. Slaps: Self-supervision improves structure learning for graph neural networks. In Advances in Neural Information Processing Systems, pages 22667-22681, 2021.
[11] Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. Learning discrete structures for graph neural networks. In Proceedings of the 36th International Conference on Machine Learning, pages 1972-1982, 2019.
[12] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, 2017.
[13] Minyang Hu, Hong Chang, Bingpeng Ma, and Shiguang Shan. Learning continuous graph structure with bilevel programming for graph neural networks. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pages 3057-3063, 2022.
[14] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In Advances in Neural Information Processing Systems, pages 22118-22133, 2020.
[15] Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo. Semi-supervised learning with graph learning-convolutional networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[16] Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 66-74, 2020.
[17] Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael M. Bronstein. Differentiable graph module (dgm) for graph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):1606-1617, 2023.
[18] Byung-Hoon Kim, Jong Chul Ye, and Jae-Jin Kim. Learning dynamic graph representation of brain connectome with spatio-temporal attention. In Advances in Neural Information Processing Systems, pages 4314-4327, 2021.
[19] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations, 2017.
[20] Changsheng Li, Xiangfeng Wang, Weishan Dong, Junchi Yan, Qingshan Liu, and Hongyuan Zha. Joint active learning with feature selection via cur matrix decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6):1382-1396, 2019.
[21] Qimai Li, Zhichao Han, and Xiao-ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018.
[22] Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang. Adaptive graph convolutional neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018.
[23] Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, and Jiliang Tang. Graph neural networks with adaptive residual. In Advances in Neural Information Processing Systems, pages 9720-9733, 2021.