Graph signal processing is a fast growing field where classical signal processing tools developed in the Euclidean domain have been generalised to irregular domains such as graphs. Below you can find a (non-exhaustive) list of useful resources in the field of graph signal processing.

- Graph Signal Analysis & Learning Workshop 2024
- Graph Signal Processing Workshop 2024
- Graph Signal Processing Workshop 2023
- IEEE SPM Special Issue on Graph Signal Processing (November 2020)
- Graph Signal Processing Workshop 2020
- IEEE GlobalSIP 2019 Symposium on Graph Signal Processing
- Graph Signal Processing Workshop 2019
- IEEE GlobalSIP 2018 Symposium on Graph Signal Processing
- Graph Signal Processing Workshop 2018
- Proceedings of the IEEE Special Issue on Applications of Graph Theory (May 2018)
- IEEE GlobalSIP 2017 Symposium on Graph Signal Processing
- IEEE TSIPN Special Issue on Graph Signal Processing (September 2017)
- IEEE JSTSP Special Issue on Graph Signal Processing (September 2017)
- Graph Signal Processing Workshop 2017
- IEEE GlobalSIP 2016 Symposium on Signal and Information Processing Over Networks
- Graph Signal Processing Workshop 2016
- IEEE GlobalSIP 2013 Symposium on Graph Signal Processing

- Introductory article
- 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. - Sandryhaila and Moura, "Big data analysis with signal processing on graphs",
*IEEE SPM*, vol. 31, no. 5, pp. 80-90, September 2014. - 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., "The emerging field of signal processing on graphs",
- Overview talk
- Vandergheynst, "Harmonic analysis on graphs and networks",
*Gretsi 2014*. - Ortega, "Signal processing on graphs: Recent results, challenges and applications",
*IEEE ICIP 2013 Plenary*. - Ortega, "Graph signal processing: An introductory overview",
*GSP Workshop 2016*. - Marques et al., "Graph signal processing: Fundamentals and applications to diffusion processes",
*IEEE ICASSP 2017 Tutorial*.

- Vandergheynst, "Harmonic analysis on graphs and networks",
- Transform/dictionary design for graph signals
- Vandergheynst and Shuman, "Wavelets on graphs, an introduction",
*Université de Provence*, November 2011. - Shuman, "Dictionary design for graph signal processing",
*GSP Workshop 2016*. - Shuman, "Localized spectral graph filter frames",
*IEEE SPM*, vol. 37, no. 6, pp. 43-63, November 2020.

- Vandergheynst and Shuman, "Wavelets on graphs, an introduction",
- Topology inference
- Rabbat, "Inferring network structure from indirect observations",
*GSP Workshop 2016*. - Dong, "Learning graphs from data: A signal processing perspective",
*GSP Workshop 2017*. - Giannakis et al., "Topology identification and learning over graphs",
*Proceedings of the IEEE*, vol. 106, no. 5, pp. 787-807, May 2018. - 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.

- Rabbat, "Inferring network structure from indirect observations",
- Graph neural networks
- Ribeiro and Gama, "Graph neural networks",
*IEEE ICASSP 2020 Tutorial*. - 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.

- Ribeiro and Gama, "Graph neural networks",
- Application
- 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.

- Cheung et al., "Graph spectral image processing",

- Graph signal processing: GSPBox PyGSP GraSP
- Spectral graph wavelet transform (SGWT): SGWT toolbox PySGWT

Geometric deep learning is a new field where deep learning techniques have been generalised to geometric domains such as graphs and manifolds. As such, it has an intimate relationship with the field of graph signal processing. Below you can find a (non-exhaustive) list of useful resources in the field of geometric deep learning, and more broadly representation learning on graphs and relational reasoning.

- Learning on Graphs and Geometry Seminar Series (Oxford)
- Learning on Graphs and Geometry Reading Group (Valence)
- Graph Representation Learning Reading Group (Mila)
- Data Science on Graphs Webinar Series (IEEE SPS)
- Networks Seminar (Oxford)

- ICML 2024 Workshop on Geometry-Grounded Representation Learning and Generative Modeling
- NeurIPS 2023 Workshop on New Frontiers in Graph Learning
- Learning on Graphs Conference 2023
- ICML 2023 Workshop on Topology, Algebra, and Geometry in Machine Learning
- NeurIPS 2022 Workshop on New Frontiers in Graph Learning
- Learning on Graphs Conference 2022
- KDD 2022 Workshop on Deep Learning on Graphs: Methods and Applications
- 17th International Workshop on Mining and Learning with Graphs
- ICML 2022 Workshop on Topology, Algebra, and Geometry in Machine Learning
- ICLR 2022 Workshop on Geometrical and Topological Representation Learning
- KDD 2021 Workshop on Deep Learning on Graphs: Methods and Applications
- ICLR 2021 Workshop on Geometrical and Topological Representation Learning
- KDD 2020 Workshop on Deep Learning on Graphs: Methods and Applications
- 16th International Workshop on Mining and Learning with Graphs
- ICML 2020 Workshop on Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond
- ICML 2020 Workshop on Graph Representation Learning and Beyond
- ICLR 2020 Workshop on Representation Learning on Graphs and Manifolds
- NeurIPS 2019 Workshop on Graph Representation Learning
- KDD 2019 Workshop on Deep Learning on Graphs: Methods and Applications
- 15th International Workshop on Mining and Learning with Graphs
- ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data
- IPAM Workshop on Deep Geometric Learning of Big Data and Applications
- ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds
- NeurIPS 2018 Workshop on Relational Representation Learning
- 14th International Workshop on Mining and Learning with Graphs
- IPAM Workshop on New Deep Learning Techniques

- Hamilton et al., "Representation learning on graphs",
*IEEE Data Engineering Bulletin*, vol. 40, no. 3, pp. 42-74, September 2017. - Chami et al., "Machine learning on graphs",
*arXiv*, May 2020. - Bronstein et al., "Geometric deep learning",
*IEEE SPM*, vol. 34, no. 4, pp. 18-42, July 2017. - 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. - Lee et al., "Attention models in graphs",
*arXiv*, July 2018. - 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/thunlp/GNNPapers
- https://github.com/thunlp/NRLPapers
- https://github.com/DeepGraphLearning/LiteratureDL4Graph
- https://t.me/graphML/491
- https://ogb.stanford.edu
- https://chrsmrrs.github.io/datasets/