[23] Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
[24] Michael Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29, 2016.
[25] Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. Masked label prediction: Unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509, 2020.
[26] Anwar Said, Mudassir Shabbir, Saeed-Ul Hassan, Zohair Raza Hassan, Ammar Ahmed, and Xenofon Koutsoukos. On augmenting topological graph representations for attributed graphs. Applied Soft Computing, 136:110104, 2023.
[27] Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, and Carl Yang. Braingb: a benchmark for brain network analysis with graph neural networks. IEEE Transactions on Medical Imaging, 2022.
[28] Jeremy Kawahara, Colin J Brown, Steven P Miller, Brian G Booth, Vann Chau, Ruth E Grunau, Jill G Zwicker, and Ghassan Hamarneh. Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146:1038-1049, 2017.
[29] David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, and Lars Petersson. Graph-based deep learning for medical diagnosis and analysis: past, present and future. Sensors, 21(14):4758, 2021.
[30] Simon Dahan, Logan ZJ Williams, Daniel Rueckert, and Emma C Robinson. Improving phenotype prediction using long-range spatio-temporal dynamics of functional connectivity. In Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN 2021, Held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 4, pages 145-154. Springer, 2021.
[31] Xuesong Wang, Lina Yao, Islem Rekik, and Yu Zhang. Contrastive functional connectivity graph learning for population-based fmri classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 221-230. Springer, 2022.
[32] Jiaxuan You, Tianyu Du, and Jure Leskovec. Roland: graph learning framework for dynamic graphs. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2358-2366, 2022.
[33] Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430, 2018.
[34] Maria Giulia Preti, Thomas AW Bolton, and Dimitri Van De Ville. The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage, 160:41-54, 2017.
[35] Jun Li, Junyu Chen, Yucheng Tang, Ce Wang, Bennett A Landman, and S Kevin Zhou. Transforming medical imaging with transformers? a comparative review of key properties, current progresses, and future perspectives. Medical image analysis, page 102762, 2023.
[36] Kanhao Zhao, Boris Duka, Hua Xie, Desmond J Oathes, Vince Calhoun, and Yu Zhang. A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in adhd. NeuroImage, 246:118774, 2022.
[37] Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, and Pietro Lio. Dbgsl: Dynamic brain graph structure learning. arXiv preprint arXiv:2209.13513, 2022.
[38] Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W Arnold, Shaoting Zhang, and Dimitri N Metaxas. Graph convolutional networks for multi-modality medical imaging: Methods, architectures, and clinical applications. arXiv preprint arXiv:2202.08916, 2022.
[39] Giovanni Bonanno, Guido Caldarelli, Fabrizio Lillo, and Rosario N Mantegna. Topology of correlation-based minimal spanning trees in real and model markets. Physical Review E, 68(4):046130, 2003.