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Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 1903
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A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and AA Lehman · 1968
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, de Compadre RL Lopez, Gargi Debnath, Alan J Shusterman, and Corwin Hansch · 1991
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Resistance distance
Douglas J Klein and Milan Randić · 1993
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Convolution kernels on discrete structures
David Haussler · 1999
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Statistical evaluation of the predictive toxicology challenge 2000–2001
Hannu Toivonen, Ashwin Srinivasan, Ross D King, Stefan Kramer, and Christoph Helma · 2003
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Brenda, the enzyme database: updates and major new developments
Ida Schomburg, Antje Chang, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn, and Dietmar Schomburg · 2004
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Shortest-path kernels on graphs
Karsten M Borgwardt and Hans-Peter Kriegel · 2005
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Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2006
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Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten M Borgwardt · 2009
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The graphlet spectrum
Risi Kondor, Nino Shervashidze, and Karsten M Borgwardt · 2009
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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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Distance encoding–design provably more powerful gnns for structural representation learning
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec · 2009
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Strongly regular graphs
Andries E Brouwer and Willem H Haemers · 2012
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Propagation kernels: efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, and Kristian Kersting · 2016
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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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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 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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Weisfeiler-lehman neural machine for link prediction
Graph warp module: an auxiliary module for boosting the power of graph neural networks
Katsuhiko Ishiguro, Shin-ichi Maeda, and Masanori Koyama · 2019
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Hongyang Gao and Shuiwang Ji · 2019
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Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris, Gaurav Rattan, and Petra Mutzel · 2020
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein · 2020
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Dominique Beaini, Saro Passaro, Vincent Létourneau, William L Hamilton, Gabriele Corso, and Pietro Liò · 2020
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Muhan Zhang and Yixin Chen · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
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Characterizing the expressive power of invariant and equivariant graph neural networks
Waïss Azizian and Marc Lelarge · 2020
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Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
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Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2020
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The surprising power of graph neural networks with random node initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe, and Thomas Lukasiewicz · 2020
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Building powerful and equivariant graph neural networks with structural message-passing
Clément Vignac, Andreas Loukas, and Pascal Frossard · 2020
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Revisiting graph neural networks for link prediction
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin · 2020
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Inductive matrix completion based on graph neural networks
Muhan Zhang and Yixin Chen · 2020
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Pim de Haan, Taco Cohen, and Max Welling · 2020
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k-hop graph neural networks
Giannis Nikolentzos, George Dasoulas, and Michalis Vazirgiannis · 2020
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Graph meta learning via local subgraphs
Kexin Huang and Marinka Zitnik · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Orbnet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R Manby, and Thomas F Miller III · 2020
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Deep graph neural networks with shallow subgraph samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, and Ren Chen · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
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Graph convolutions that can finally model local structure
Rémy Brossard, Oriel Frigo, and David Dehaene · 2020
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Identity-aware graph neural networks
Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, and Jure Leskovec · 2021
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Parameterized hypercomplex graph neural networks for graph classification
Tuan Le, Marco Bertolini, Frank Noé, and Djork-Arné Clevert · 2021
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