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• #GRAPH SIGNAL PROCESSING SERIES#

17th International Workshop on Mining and Learning with Graphs.KDD 2022 Workshop on Deep Learning on Graphs: Methods and Applications.Learning on Graphs Conference (LoG) 2022.ICML 2023 Workshop on Topology, Algebra, and Geometry in Machine Learning.Learning on Graphs Conference (LoG) 2023.Graph Representation Learning Reading Group.Learning on Graphs and Geometry Reading Group.

#GRAPH SIGNAL PROCESSING SERIES#

Learning on Graphs and Geometry Seminar Series.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. As such, it has an intimate relationship with the field of graph signal processing. Geometric deep learning is a new field where deep learning techniques have been generalised to geometric domains such as graphs and manifolds. Spectral graph wavelet transform (SGWT): SGWT toolbox PySGWT.Graph signal processing: GSPBox PyGSP GraSP.Huang et al., " A graph signal processing perspective on functional brain imaging", Proceedings of the IEEE, vol.Cheung et al., " Graph spectral image processing", Proceedings of the IEEE, vol.Cheung et al., " Graph signal processing and deep learning", IEEE SPM, vol.Gama et al., " Graphs, convolutions, and neural networks", IEEE SPM, vol.Ribeiro and Gama, " Graph neural networks", IEEE ICASSP 2020 Tutorial.Dong et al., " Learning graphs from data", IEEE SPM, vol.Mateos et al., " Connecting the dots", IEEE SPM, vol.Giannakis et al., " Topology identification and learning over graphs", Proceedings of the IEEE, vol.Dong, " Learning graphs from data: A signal processing perspective", GSP Workshop 2017.Rabbat, " Inferring network structure from indirect observations", GSP Workshop 2016.Shuman, " Localized spectral graph filter frames", IEEE SPM, vol.