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Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs.
Alchemy: A quantum chemistry dataset for benchmarking AI models
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The reduction of a graph to canonical form and the algebra which appears therein
B. Weisfeiler and A. Leman · 1968
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Lectures on graph isomorphism
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Canonical labelling of graphs in linear average time
L. Babai and L. Kucera · 1979
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Describing graphs: A first-order approach to graph canonization
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An optimal lower bound on the number of variables for graph identifications
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Chemnet: A novel neural network based method for graph/property mapping
D. B. Kireev · 1995
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A neural device for searching direct correlations between structures and properties of chemical compounds
I. I. Baskin, V. A. Palyulin, and N. S. Zefirov · 1997
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Supervised neural networks for the classification of structures
A. Sperduti and A. Starita · 1997
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Weisfeiler-Lehman refinement requires at least a linear number of iterations
M. Fürer · 2001
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Network biology: Understanding the cell’s functional organization
A.-L. Barabasi and Z. N. Oltvai · 2004
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Principal neighbourhood aggregation for graph nets
G. Corso, L. Cavalleri, D. Beaini, P. Liò, and P. Velickovic · 2004
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Automatic generation of complementary descriptors with molecular graph networks
C. Merkwirth and T. Lengauer · 2005
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A new neural network model for contextual processing of graphs
A. Micheli and A. S. Sestito · 2005
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TUDataset: A collection of benchmark datasets for learning with graphs
C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann · 2007
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Neural network for graphs: A contextual constructive approach
A. Micheli · 2009
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun · 2009
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Implicit differentiation by perturbation
J. Domke · 2010
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Networks, Crowds, and Markets: Reasoning About a Highly Connected World
D. Easley and J. Kleinberg · 2010
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Perturb-and-map random fields: Using discrete optimization to learn and sample from energy models
G. Papandreou and A. L. Yuille · 2011
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Spectral networks and deep locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, O. Dral, P., M. Rupp, and O. A. von Lilienfeld · 2014
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Graph isomorphism in quasipolynomial time
L. Babai · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, B. X., and P. Vandergheynst · 2016
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Exact sampling with integer linear programs and random perturbations
C. Kim, A. Sabharwal, and S. Ermon · 2016
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Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 2016
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Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 2016
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Descriptive Complexity, Canonisation, and Definable Graph Structure Theory
M. Grohe · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 2017
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Predicting organic reaction outcomes with Weisfeiler-Lehman network
W. Jin, C. W. Coley, R. Barzilay, and T. S. Jaakkola · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh · 2017
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Geometric deep learning on graphs and manifolds using mixture model CNNs
F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein · 2017
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Glocalized Weisfeiler-Lehman kernels: Global-local feature maps of graphs
C. Morris, K. Kersting, and P. Mutzel · 2017
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
M. Simonovsky and N. Komodakis · 2017
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
G. Tucker, A. Mnih, C. J. Maddison, D. Lawson, and J. Sohl-Dickstein · 2017
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Towards sparse hierarchical graph classifiers
C. Cangea, P. Velickovic, N. Jovanovic, T. Kipf, and P. Liò · 2018
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Backpropagation through the void: Optimizing control variates for black-box gradient estimation
W. Grathwohl, D. Choi, Y. Wu, G. Roeder, and D. Duvenaud · 2018
Cited alongside, same era.
Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Directional message passing for molecular graphs
S. G. J. Klicpera, J. Groß · 2020
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A survey on graph kernels
N. M. Kriege, F. D. Johansson, and C. Morris · 2020
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Distance encoding: Design provably more powerful neural networks for graph representation learning
P. Li, Y. Wang, H. Wang, and J. Leskovec · 2020
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On learning sets of symmetric elements
H. Maron, O. Litany, G. Chechik, and E. Fetaya · 2020
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Graph homomorphism convolution
H. NT and T. Maehara · 2020
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Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
R. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and C. Yixin · 2018
Cited alongside, same era.
MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. V. Steeg, and A. Galstyan · 2019
Cited alongside, same era.
Cormorant: Covariant molecular neural networks
B. M. Anderson, T. Hy, and R. Kondor · 2019
Cited alongside, same era.
Hyperbolic graph convolutional neural networks
I. Chami, Z. Ying, C. Ré, and J. Leskovec · 2019
Cited alongside, same era.
M. B. Paulus, D. Choi, D. Tarlow, A. Krause, and C. J. Maddison · 2020
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Optimizing rank-based metrics with blackbox differentiation
M. Rolinek, V. Musil, A. Paulus, M. Vlastelica, C. Michaelis, and G. Martius · 2020
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DropEdge: Towards deep graph convolutional networks on node classification
Y. Rong, W. Huang, T. Xu, and J. Huang · 2020
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Random features strengthen graph neural networks
R. Sato, M. Yamada, and H. Kashima · 2020
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A deep learning approach to antibiotic discovery
J. Stokes, K. Yang, K. Swanson, W. Jin, A. Cubillos-Ruiz, N. Donghia, C. MacNair, S. French, L. Carfrae, Z. Bloom-Ackerman, V. Tran, A. Chiappino-Pepe, A. Badran, I. Andrews, E. Chory, G. Church, E. Brown, T. Jaakkola, R. Barzilay, and J. Collins · 2020
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Building powerful and equivariant graph neural networks with structural message-passing
C. Vignac, A. Loukas, and P. Frossard · 2020
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Large-scale graph representation learning with very deep gnns and self-supervision
R. Addanki, P. W. Battaglia, D. Budden, A. Deac, J. Godwin, T. Keck, W. L. S. Li, A. Sanchez-Gonzalez, J. Stott, S. Thakoor, and P. Velickovic · 2021
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Breaking the limits of message passing graph neural networks
M. Balcilar, P. Héroux, B. Gaüzère, P. Vasseur, S. Adam, and P. Honeine · 2021
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Graph neural networks with local graph parameters
P. Barceló, F. Geerts, J. L. Reutter, and M. Ryschkov · 2021
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Equivariant subgraph aggregation networks
B. Bevilacqua, F. Frasca, D. Lim, B. Srinivasan, C. Cai, G. Balamurugan, M. M. Bronstein, and H. Maron · 2021
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Reconstruction for powerful graph representations
L. Cotta, C. Morris, and B. Ribeiro · 2021
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GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings
M. Fey, J. E. Lenssen, F. Weichert, and J. Leskovec · 2021
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Understanding pooling in graph neural networks
D. Grattarola, D. Zambon, F. M. Bianchi, and C. Alippi · 2021
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The logic of graph neural networks
M. Grohe · 2021
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Topological graph neural networks
M. Horn, E. D. Brouwer, M. Moor, Y. Moreau, B. Rieck, and K. M. Borgwardt · 2021
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Highly accurate protein structure prediction with AlphaFold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 2021
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Gemnet: Universal directional graph neural networks for molecules
J. Klicpera, F. Becker, and S. Günnemann · 2021
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Training graph neural networks with 1000 layers
G. Li, M. Müller, B. Ghanem, and V. Koltun · 2021
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Weisfeiler and Leman go machine learning: The story so far
C. Morris, Y. L., H. Maron, B. Rieck, N. M. Kriege, M. Grohe, M. Fey, and K. Borgwardt · 2021
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Implicit MLE: backpropagating through discrete exponential family distributions
M. Niepert, P. Minervini, and L. Franceschi · 2021
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DropGNN: Random dropouts increase the expressiveness of graph neural networks
P. A. Papp, L. F. K. Martinkus, and R. Wattenhofer · 2021
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Neural trees for learning on graphs
R. Talak, S. Hu, L. Peng, and L. Carlone · 2021
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Autobahn: Automorphism-based graph neural nets
E. H. Thiede, W. Zhou, and R. Kondor · 2021
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Graph learning with 1D convolutions on random walks
J. Tönshoff, M. Ritzert, H. Wolf, and M. Grohe · 2021
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Identity-aware graph neural networks
J. You, J. Gomes-Selman, R. Ying, and J. Leskovec · 2021
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M. Zhang and P. Li · 2021
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From stars to subgraphs: Uplifting any GNN with local structure awareness
L. Zhao, W. Jin, L. Akoglu, and N. Shah · 2021
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in gnns
C. Bodnar, F. D. Giovanni, B. P. Chamberlain, P. Liò, and M. M. Bronstein · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
G. Bouritsas, F. Frasca, S. P. Zafeiriou, and M. Bronstein · 2022
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Understanding and extending subgraph GNNs by rethinking their symmetries
F. Frasca, B. Bevilacqua, M. M. Bronstein, and H. Maron · 2022
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Expressiveness and approximation properties of graph neural networks
F. Geerts and J. L. Reutter · 2022
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SpeqNets: Sparsity-aware permutation-equivariant graph networks
C. Morris, G. Rattan, S. Kiefer, and S. Ravanbakhsh · 2022
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A theoretical comparison of graph neural network extensions
P. A. Papp and R. Wattenhofer · 2022
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