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Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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An optimal lower bound on the number of variables for graph identifications
Jin-yi Cai, Martin Fürer, and Neil Immerman · 1992
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Emergence of scaling in random networks
Albert-Laszlo Barabasi and Reka Albert · 1999
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
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The Graph Neural Network Model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2013
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Discrete signal processing on graphs
Aliaksei Sandryhaila and José MF Moura · 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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Gated graph sequence neural networks, 2015
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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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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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel · 2016
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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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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Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov · 2017
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Graph neural networks: A review of methods and applications, 2018
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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Monophily in social networks introduces similarity among friends-of-friends
Kristen M. Altenburger and Johan Ugander · 2018
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Motifnet: A motif-based graph convolutional network for directed graphs
Federico Monti, Karl Otness, and Michael M. Bronstein · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Fast graph representation learning with pytorch geometric, 2019
Matthias Fey and Jan Eric Lenssen · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Hoang Nt and Takanori Maehara · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Improving graph neural networks with simple architecture design
Sunil Kumar Maurya, Xin Liu, and Tsuyoshi Murata · 2021
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Weisfeiler and lehman go topological: Message passing simplicial networks
Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lió, and Michael Bronstein · 2021
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2022
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Learning to untangle genome assembly with graph convolutional networks
Lovro Vrček, Xavier Bresson, Thomas Laurent, Martin Schmitz, and Mile Šikić · 2022
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Revisiting heterophily for graph neural networks
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup · 2022
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Spectral-based graph convolutional network for directed graphs, 2019
Yi Ma, Jianye Hao, Yaodong Yang, Han Li, Junqi Jin, and Guangyong Chen · 2019
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edgnn: a simple and powerful GNN for directed labeled graphs
Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, and Maria Gabrani · 2019
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MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
Cited alongside, same era.
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
Cited alongside, same era.
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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Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar · 2020
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, and Michael M Bronstein · 2022
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Graph neural networks as gradient flows
Francesco Di Giovanni, James Rowbottom, Benjamin P Chamberlain, Thomas Markovich, and Michael M Bronstein · 2022
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Finding global homophily in graph neural networks when meeting heterophily
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian · 2022
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Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang · 2022
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Weisfeiler and leman go relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris, and Miguel Romero Orth · 2022
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Directed graph auto-encoders
G. Kollias, Vasileios Kalantzis, Tsuyoshi Id’e, Aurélie C. Lozano, and Naoki Abe · 2022
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron · 2022
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Adaptive filters for low-latency and memory-efficient graph neural networks
Shyam A. Tailor, Felix Opolka, Pietro Lio, and Nicholas Donald Lane · 2022
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PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
Yixuan He, Xitong Zhang, Junjie Huang, Benedek Rozemberczki, Mihai Cucuringu, and Gesine Reinert · 2022
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Transformers meet directed graphs
Simon Geisler, Yujia Li, Daniel J Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, and Cosmin Paduraru · 2023
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A fractional graph laplacian approach to oversmoothing, 2023
Sohir Maskey, Raffaele Paolino, Aras Bacho, and Gitta Kutyniok · 2023
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A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova · 2023
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Gradient gating for deep multi-rate learning on graphs
T Konstantin Rusch, Benjamin P Chamberlain, Michael W Mahoney, Michael M Bronstein, and Siddhartha Mishra · 2023
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When do graph neural networks help with node classification: Investigating the homophily principle on node distinguishability
Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, and Doina Precup · 2023
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