- Oxford Network Seminar
- Graph Representation Learning Reading Group at Mila
- Learning on Graphs and Geometry Reading Group (LoGaG)
- Data sciEnce on GrAphS (DEGAS)
- Molecular Modeling And Drug Discovery (M2D2)
- Hamilton et al., "Representation learning on graphs", arXiv, September 2017.
- Chami et al., "Machine learning on graphs", arXiv, May 2020.
- Bronstein et al., "Geometric deep learning: Going beyond Euclidean data", arXiv, November 2016.
- Bronstein et al., "Geometric deep learning: Grids, groups, graphs, geodesics, and gauges", arXiv, April 2021.
- Battaglia et al., "Relational inductive biases, deep learning, and graph networks", arXiv, June 2018.
- Wu et al., "A comprehensive survey on graph neural networks", arXiv, January 2019.
- Sun et al., "Adversarial attack and defense on graph data", arXiv, December 2018.
- Jin et al., "Adversarial attacks and defenses on graphs", arXiv, March 2020.
- http://geometricdeeplearning.com
- https://github.com/DeepGraphLearning/LiteratureDL4Graph
- https://github.com/thunlp/GNNPapers
- https://github.com/thunlp/NRLPapers
- Link to join the Graph Machine Learning Telegram group
- https://ogb.stanford.edu
- https://chrsmrrs.github.io/datasets/
- Shuman et al., "The emerging field of signal processing on graphs", IEEE SPM, vol. 30, no. 3, pp. 83-98, May 2013.
- Sandryhaila and Moura, "Discrete signal processing on graphs", IEEE TSP, vol. 61, no. 7, pp. 1644-1656, April 2013.
- Ortega et al., "Graph signal processing", Proceedings of the IEEE, vol. 106, no. 5, pp. 808-828, May 2018.
- Dong et al., "Graph signal processing for machine learning", IEEE SPM, vol. 37, no. 6, pp. 117-127, November 2020.
- Shuman et al., "Vertex-frequency analysis on graphs", Applied and Computational Harmonic Analysis, vol. 40, no. 2, pp. 260-291, March 2016.
- Shuman, "Localized spectral graph filter frames", IEEE SPM, vol. 37, no. 6, pp. 43-63, November 2020.
- Mateos et al., "Connecting the dots", IEEE SPM, vol. 36, no. 3, pp. 16-43, May 2019.
- Dong et al., "Learning graphs from data", IEEE SPM, vol. 36, no. 3, pp. 44-63, May 2019.
- Gama et al., "Graphs, convolutions, and neural networks", IEEE SPM, vol. 37, no. 6, pp. 128-138, November 2020.
- Cheung et al., "Graph signal processing and deep learning", IEEE SPM, vol. 37, no. 6, pp. 139-149, November 2020.
- Cheung et al., "Graph spectral image processing", Proceedings of the IEEE, vol. 106, no. 5, pp. 907-930, May 2018.
- Huang et al., "A graph signal processing perspective on functional brain imaging", Proceedings of the IEEE, vol. 106, no. 5, pp. 868-885, May 2018.
- Graph signal processing: GSPBox PyGSP GraSP
- Spectral graph wavelet transform (SGWT): SGWT toolbox PySGWT