A curated list of topological deep learning (TDL) resources and links.
A curated list of Topological Deep Learning (TDL) tools and resources.
Simplicial Neural Networks. Stefania Ebli, Michaël Defferrard, Gard Spreemann. NeurIPS 2020 Workshop TDA and Beyond. Paper, Code ,
Simplicial 2-Complex Convolutional Neural Nets. Eric Bunch, Qian You, Glenn Fung, Vikas Singh. NeurIPS 2020 Workshop TDA and Beyond. Paper, Code
Cell complex neural networks. Mustafa Hajij, Kyle Istvan, and Ghada Zamzmi. NeurIPS Workshop on Topological Data Analysis and Beyond, 2020. Paper,
Principled simplicial neural networks for trajectory prediction. Roddenberry, T. Mitchell, Nicholas Glaze, and Santiago Segarra. ICML 2021. Paper, Code
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang, Nina Otter, Guido Montúfar, Pietro Liò, Michael Bronstein. ICML 2021. Paper, Code ,
Weisfeiler and Lehman Go Cellular: CW Networks. Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yu Guang Wang, Pietro Liò, Guido Montúfar, Michael Bronstein. NeurIPS 2021. Paper, Code ,
Simplicial Attention Neural Networks. Lorenzo Giusti, Claudio Battiloro, Paolo Di Lorenzo, Stefania Sardellitti, Sergio Barbarossa. arXiv 2022. Paper, Code
Simplicial Attention Networks. Christopher Wei Jin Goh, Cristian Bodnar, Pietro Liò. ICLR 2022 Workshop on Geometrical and Topological Representation Learning. Paper, Code
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein. NeurIPS 2022. Paper, Code ,
Simplicial Convolutional Neural Networks. Maosheng Yang, Elvin Isufi, Geert Leus. ICASSP 2022. Paper, Code
Sheaf Neural Networks with Connection Laplacians. Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, Pietro Liò. ICML 2022 Workshop on Topology, Algebra, and Geometry in Machine Learning. Paper
Sheaf Attention Networks. Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Pietro Liò. NeurIPS 2022 NeurReps Workshop. Paper
Cell Attention Networks. Lorenzo Giusti, Claudio Battiloro, Lucia Testa, Paolo Di Lorenzo, Stefania Sardellitti, Sergio Barbarossa. IEEE IJCNN 2023. Paper, Code
Surfing on the Neural Sheaf. Julian Suk, Lorenzo Giusti, Tamir Hemo, Miguel Lopez, Konstantinos Barmpas, Cristian Bodnar. NeurIPS 2022 NeurReps Workshop. Paper
Architectures of Topological Deep Learning: A Survey on Topological Neural Networks. Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane. Paper
Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back. Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro. Paper, Code
Topological Graph Neural Networks. Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, and Karsten Borgwardt. ICLR 2022. Paper, Code ,
Topological Autoencoders. Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt. ICML 2020. Paper, Code
E(n) Equivariant Message Passing Simplicial Networks. Floor Eijkelboom, Rob Hesselink, Erik Bekkers. ICML 2023. Paper
CIN++: Enhancing Topological Message Passing. Lorenzo Giusti, Teodora Reu, Francesco Ceccarelli, Cristian Bodnar, Pietro Liò. Paper, Code
Simplicial Hopfield networks. Thomas F Burns, Tomoki Fukai. ICLR 2023. Paper, Code,
Topological Deep Learning: Graphs, Complexes, Sheaves. Cristian Bodnar. Thesis
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes. Quang Truong and Peter Chin. Paper
Generalized simplicial attention neural networks. Claudio Battiloro, Lucia Testa, Lorenzo Giusti, Stefania Sardellitti, Paolo Di Lorenzo, Sergio Barbarossa. Paper
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. Mustafa Hajij, Mathilde Papillon, Florian Frantzen et al. Paper, Code
Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions. Lorenzo Giusti. Thesis
Discrete Exterior Calculus. Anil N. Hirani. Caltech Library 2003. PhD Thesis
Discrete Calculus: Applied Analysis on Graphs for Computational Science. Leo J. Grady, Jonathan R. Polimeni. Springer 2010 . Book
Discrete Differential Forms for Computational Modeling. Mathieu Desbrun, Eva Kanso & Yiying Tong. SIGGRAPH 2006. Paper