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Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
Learning graph-level representation for drug discovery
Li, J., Cai, D., and He, X · 2017
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M · 2018
Cited alongside, same era.
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Steeg, G. V., and Galstyan, A · 2019
Cited alongside, same era.
What graph neural networks cannot learn: depth vs width
Loukas, A · 2019
Cited alongside, same era.
Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
Hierarchical inter-message passing for learning on molecular graphs
Fey, M., Yuen, J. G., and Weichert, F · 2020
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Neural message passing on high order paths
Flam-Shepherd, D., Wu, T., Friederich, P., and Aspuru-Guzik, A · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Strategies for pre-training graph neural networks
Hu*, W., Liu*, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
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Flag: Adversarial data augmentation for graph neural networks
Kong, K., Li, G., Ding, M., Wu, Z., Zhu, C., Ghanem, B., Taylor, G., and Goldstein, T · 2020
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Cited alongside, same era.
Relational pooling for graph representations
Murphy, R. L., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
Graphnorm: A principled approach to accelerating graph neural network training
Cai, T., Luo, S., Xu, K., He, D., Liu, T.-y., and Wang, L · 2020
Cited alongside, same era.
Can graph neural networks count substructures?
Chen, Z., Chen, L., Villar, S., and Bruna, J · 2020
Cited alongside, same era.
Li, G., Xiong, C., Thabet, A., and Ghanem, B · 2020
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k-hop graph neural networks
Nikolentzos, G., Dasoulas, G., and Vazirgiannis, M · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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