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Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph.
A set of measures of centrality based on betweenness
Linton C Freeman · 1977
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The ricci flow on surfaces
Richard Hamilton · 1988
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Encoding labeled graphs by labeling RAAM
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Learning task-dependent distributed representations by backpropagation through structure
Christoph Goller and Andreas Kuchler · 1996
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Spectral graph theory
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Supervised neural networks for the classification of structures
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The anatomy of a large-scale hypertextual web search engine
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A general framework for adaptive processing of data structures
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Automating the construction of internet portals with machine learning
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Differentiable graph module (dgm) graph convolutional networks
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Discrete and computational geometry, 2003
Robin Forman · 2003
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Finite extinction time for the solutions to the ricci flow on certain three-manifolds
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Sign: Scalable inception graph neural networks
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Diffusion maps
Ronald R Coifman and Stéphane Lafon · 2006
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Four proofs for the cheeger inequality and graph partition algorithms
Fan Chung · 2007
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Ricci curvature of metric spaces
Yann Ollivier · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Ricci curvature of markov chains on metric spaces
Yann Ollivier · 2009
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Social influence analysis in large-scale networks
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang · 2009
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Ricci curvature of graphs
Yong Lin, Linyuan Lu, and Shing-Tung Yau · 2011
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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and UMD EDU · 2012
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Volume and diameter of a graph and ollivier’s ricci curvature
Seong-Hun Paeng · 2012
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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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Ollivier’s ricci curvature, local clustering and curvature-dimension inequalities on graphs
Jürgen Jost and Shiping Liu · 2014
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Hyperbolic graph convolutional neural networks
Ines Chami, Rex Ying, Christopher Ré, and Jure Leskovec · 2019
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Diffusion improves graph learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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Hyperbolic graph neural networks
Qi Liu, Maximilian Nickel, and Douwe Kiela · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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A lower bound for the smallest eigenvalue of the laplacian
Jeff Cheeger · 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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Forman curvature for complex networks
R P Sreejith, Karthikeyan Mohanraj, Jürgen Jost, Emil Saucan, and Areejit Samal · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Community detection on networks with ricci flow
Chien-Chun Ni, Yu-Yao Lin, Feng Luo, and Jie Gao · 2019
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Revisiting graph neural networks: All we have is low-pass filters
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Geom-gcn: Geometric graph convolutional networks
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Dynamic graph CNN for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Bayesian graph convolutional neural networks for semi-supervised classification
Y. Zhang, S. Pal, M. Coates, and D. Üstebay · 2019
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Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2020
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Adversarial attacks on graph neural networks: Perturbations and their patterns
Daniel Zügner, Oliver Borchert, Amir Akbarnejad, and Stephan Günnemann · 2020
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Network geometry
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
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