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Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data.
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“PyTorch Lightning”, 2019
William Falcon and The PyTorch Lightning team · 2019
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“Fast Graph Representation Learning with PyTorch Geometric”
Matthias Fey and Jan. Lenssen · 2019
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“Toward a spectral theory of cellular sheaves”
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“Self-Attention Graph Pooling”
Junhyun Lee, Inyeop Lee and Jaewoo Kang · 2019
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“(Hyper)Graph Embedding and Classification via Simplicial Complexes”
Alessio Martino, Alessandro Giuliani and Antonello Rizzi · 2019
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Adam Paszke et al · 2019
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“Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks”
Minjie Wang et al · 2019
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“Topological Signal Processing Over Simplicial Complexes”
Sergio Barbarossa and Stefania Sardellitti · 2020
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Federico Battiston et al · 2020
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Filippo Bianchi, Daniele Grattarola and Cesare Alippi · 2020
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“Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling”
Filippo Bianchi, Daniele Grattarola, Lorenzo Livi and Cesare Alippi · 2020
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“Logic and the 2-Simplicial Transformer”
James Clift, Dmitry Doryn, Daniel Murfet and James Wallbridge · 2020
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“Principal Neighbourhood Aggregation for Graph Nets”
Gabriele Corso et al · 2020
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Songgaojun Deng et al · 2020
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Alexey Dosovitskiy et al · 2020
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Yue Gao et al · 2020
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“Cell Complex Neural Networks”
Mustafa Hajij, Kyle Istvan and Ghada Zamzmi · 2020
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“Simplicial degree in complex networks. Applications of topological data analysis to network science”
“Sheaf Neural Networks with Connection Laplacians”, 2022
Federico Barbero et al · 2022
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“Pyramidal Reservoir Graph Neural Network”
Filippo Bianchi, Claudio Gallicchio and Alessio Micheli · 2022
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“Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs”
Cristian Bodnar et al · 2022
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“Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting”
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou and Michael. Bronstein · 2022
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“Graph Neural Networks with Learnable Structural and Positional Representations”
Vijay Dwivedi et al · 2022
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Daniel Hernández, Juan Hernández-Serrano and Darío Sánchezómez · 2020
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“Open Graph Benchmark: Datasets for Machine Learning on Graphs”
Weihua Hu et al · 2020
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“Hypergraph attention networks for multimodal learning”
Eun-Sol Kim et al · 2020
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“Hodge Laplacians on graphs”
Lek-Heng Lim · 2020
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“(Hyper)graph Kernels over Simplicial Complexes”
Alessio Martino and Antonello Rizzi · 2020
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“Self-supervised graph transformer on large-scale molecular data”
Yu Rong et al · 2020
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“Random Walks on Simplicial Complexes and the Normalized Hodge 1-Laplacian”
Michael. Schaub et al · 2020
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Lorenzo Giusti et al · 2022
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“Simplicial attention networks”
Christopher Goh, Cristian Bodnar and Pietro Lio · 2022
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“Simplicial Complex Representation Learning”
Mustafa Hajij et al · 2022
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“Topological deep learning: Going beyond graph data”
Mustafa Hajij et al · 2022
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“A survey on vision transformer”
Kai Han et al · 2022
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“An Analysis of Virtual Nodes in Graph Neural Networks for Link Prediction (Extended Abstract)”
EunJeong Hwang, Veronika Thost, Shib Dasgupta and Tengfei Ma · 2022
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“Transformer for graphs: An overview from architecture perspective”
Erxue Min et al · 2022
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“Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction”
Erxue Min et al · 2022
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“Recipe for a General, Powerful, Scalable Graph Transformer”
Ladislav Rampasek et al · 2022
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“Signal processing on cell complexes”
T Roddenberry, Michael Schaub and Mustafa Hajij · 2022
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“Topological Signal Representation and Processing over Cell Complexes”
Stefania Sardellitti and Sergio Barbarossa · 2022
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“Tangent Bundle Filters and Neural Networks: From Manifolds to Cellular Sheaves and Back”
C. Battiloro et al · 2023
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“Topological Slepians: Maximally Localized Representations of Signals Over Simplicial Complexes”
Claudio Battiloro, Paolo Di and Sergio Barbarossa · 2023
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“Benchmarking Graph Neural Networks”
Vijay Dwivedi et al · 2023
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“Cell attention networks”
Lorenzo Giusti et al · 2023
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“Combinatorial complexes: bridging the gap between cell complexes and hypergraphs”
Mustafa Hajij et al · 2023
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“pyt-team/TopoNetX: TopoNetX 0.0.2”
Nina Miolane et al · 2023
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“Architectures of topological deep learning: A survey on topological neural networks”
Mathilde Papillon, Sophia Sanborn, Mustafa Hajij and Nina Miolane · 2023
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“Convolving Directed Graph Edges via Hodge Laplacian for Brain Network Analysis”
Joonhyuk Park et al · 2023
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“Topo-mlp: A simplicial network without message passing”
Karthikeyan Ramamurthy, Aldo Guzmán-Sáenz and Mustafa Hajij · 2023
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“Exphormer: Sparse transformers for graphs”
Hamed Shirzad et al · 2023
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“Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes”
Cai Zhou, Xiyuan Wang and Muhan Zhang · 2023
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“From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module”
Claudio Battiloro et al · 2024
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“Tangent bundle convolutional learning: from manifolds to cellular sheaves and back”
Claudio Battiloro et al · 2024
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“Benchmark dataset for graph classification GitHub repository” Accessed: 2024-04-14,
2024
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“ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain” Accessed: 2024-05-15,
Guillermo Bernárdez et al · 2024
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“SaNN: Simple Yet Powerful Simplicial-aware Neural Networks”
Sravanthi Gurugubelli and Sundeep Chepuri · 2024
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“TopoX: a suite of Python packages for machine learning on topological domains”
Mustafa Hajij et al · 2024
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“Simplicial Representation Learning with Neural $k$-Forms”
Kelly Maggs, Celia Hacker and Bastian Rieck · 2024
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“Position Paper: Challenges and Opportunities in Topological Deep Learning”, 2024
Theodore Papamarkou et al · 2024
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“On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers”, 2024
Cai Zhou, Rose Yu and Yusu Wang · 2024
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“Graph U-Nets”
Hongyang Gao and Shuiwang Ji · 2092
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