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Cellular sheaves equip graphs with a "geometrical" structure by assigning vector spaces and linear maps to nodes and edges.
A cellular description of the derived category of a stratified space
Allen Dudley Shepard · 1985
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Sheaf theory , volume 170
Glen E Bredon · 2012
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Sheaves in geometry and logic: A first introduction to topos theory
Saunders MacLane and Ieke Moerdijk · 2012
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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and U Edu · 2012
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Vector diffusion maps and the connection laplacian
Amit Singer and H-T Wu · 2012
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A Cheeger inequality for the graph connection laplacian
Afonso S Bandeira, Amit Singer, and Daniel A Spielman · 2013
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Categories for the working mathematician , volume 5
Saunders Mac Lane · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Sheaves, cosheaves and applications
Justin Michael Curry · 2014
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Elementary applied topology , volume 1
Robert W Ghrist · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Neural message passing for quantum chemistry
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An introduction to nonassociative algebras
Richard D Schafer · 2017
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Disentangling by subspace diffusion
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Continuous graph neural networks
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Pairnorm: Tackling oversmoothing in gnns
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Beyond low-frequency information in graph convolutional networks
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen · 2021
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Graph attention networks
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
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Learning sheaf laplacians from smooth signals
Jakob Hansen and Robert Ghrist · 2019
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Weisfeiler and Leman go neural: Higher-order graph neural networks
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Revisiting graph neural networks: all we have is low pass filters
H Nt and T Maehara · 2019
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Graph neural networks exponentially lose expressive power for node classification
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Adaptive universal generalized pagerank graph neural network
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The geometry of synchronization problems and learning group actions
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Gauge equivariant mesh CNNs: Anisotropic convolutions on geometric graphs
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Opinion dynamics on discourse sheaves
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Simple truncated svd based model for node classification on heterophilic graphs
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Is heterophily a real nightmare for graph neural networks to do node classification?
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Efficient householder transformation in pytorch, 2021
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Multi-scale attributed node embedding
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Approximate and discrete euclidean vector bundles
Luis Scoccola and Jose A Perea · 2021
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
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Sheaf neural networks with connection laplacians
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Sheaf attention networks
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Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily
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Graphworld: Fake graphs bring real insights for gnns
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Sheaf theory through examples
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