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Graph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks.
Independence, invariance and the causal markov condition
Hausman, D. M. and Woodward, J · 1999
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
Earlier work this paper cites.
Bayesian active learning for classification and preference learning
Houlsby, N., Huszár, F., Ghahramani, Z., and Lengyel, M · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., and Yan, X · 2015
Earlier work this paper cites.
Line: Large-scale information network embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q · 2015
Earlier work this paper cites.
The extreme classification repository: Multi-label datasets and code, 2016
Bhatia, K., Dahiya, K., Jain, H., Kar, P., Mittal, A., Prabhu, Y., and Varma, M · 2016
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.
node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
Earlier work this paper cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T · 2016
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
Earlier work this paper cites.
Data augmentation for low-resource neural machine translation
Fadaee, M., Bisazza, A., and Monz, C · 2017
Earlier work this paper cites.
Learning graph representations with embedding propagation
García-Durán, A. and Niepert, M · 2017
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.
Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 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.
Temporal ensembling for semi-supervised learning
Samuli, L. and Timo, A · 2017
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
Earlier work this paper cites.
Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A. L · 2018
Earlier work this paper cites.
Designing random graph models using variational autoencoders with applications to chemical design
Samanta, B., De, A., Ganguly, N., and Gomez-Rodriguez, M · 2018
Earlier work this paper cites.
Graphvae: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N · 2018
Earlier work this paper cites.
Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M · 2018
Cited alongside, same era.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T., He, T., Karypis, G., Li, J., and Zhang, Z · 2019
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Generalized data augmentation for low-resource translation
Xia, M., Kong, X., Anastasopoulos, A., and Neubig, G · 2019
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Conditional structure generation through graph variational generative adversarial nets
Yang, C., Zhuang, P., Shi, W., Luu, A., and Li, P · 2019
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Data augmentation using learned transformations for one-shot medical image segmentation
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V., and Dalca, A. V · 2019
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Graphzoom: A multi-level spectral approach for accurate and scalable graph embedding
Deng, C., Zhao, Z., Wang, Y., Zhang, Z., and Feng, Z · 2020
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Ver Steeg, G., and Galstyan, A · 2019
Cited alongside, same era.
Mixmatch: A holistic approach to semi-supervised learning
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., and Raffel, C · 2019
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
Cited alongside, same era.
Retrosynthesis prediction with conditional graph logic network
Dai, H., Li, C., Coley, C. W., Dai, B., and Song, L · 2019
Cited alongside, same era.
Batch virtual adversarial training for graph convolutional networks
Deng, Z., Dong, Y., and Zhu, J · 2019
Cited alongside, same era.
Graph adversarial training: Dynamically regularizing based on graph structure
Feng, F., He, X., Tang, J., and Chua, T.-S · 2019
Cited alongside, same era.
Graph random neural network for semi-supervised learning on graphs
Feng, W., Zhang, J., Dong, Y., Han, Y., Luan, H., Xu, Q., Yang, Q., Kharlamov, E., and Tang, J · 2020
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Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H · 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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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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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2020
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Sign: Scalable inception graph neural networks
Rossi, E., Frasca, F., Chamberlain, B., Eynard, D., Bronstein, M., and Monti, F · 2020
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A graph to graphs framework for retrosynthesis prediction
Shi, C., Xu, M., Guo, H., Zhang, M., and Tang, J · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J · 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Pre-train and learn: Preserve global information for graph neural networks
Zhu, D., Dai, X.-Y., and Chen, J · 2020
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Mathematical models of overparameterized neural networks
Fang, C., Dong, H., and Zhang, T · 2021
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Training graph neural networks with 1000 layers
Li, G., Müller, M., Ghanem, B., and Koltun, V · 2021
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Distilling self-knowledge from contrastive links to classify graph nodes without passing messages
Luo, Y., Chen, A., Yan, K., and Tian, L · 2021
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Decoupling the depth and scope of graph neural networks
Zeng, H., Zhang, M., Xia, Y., Srivastava, A., Malevich, A., Kannan, R., Prasanna, V., Jin, L., and Chen, R · 2021
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Nested graph neural networks
Zhang, M. and Li, P · 2021
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Data augmentation for graph neural networks
Zhao, T., Liu, Y., Neves, L., Woodford, O., Jiang, M., and Shah, N · 2021
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Simple spectral graph convolution
Zhu, H. and Koniusz, P · 2021
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Learning the compositional domains for generalized zero-shot learning
Dong, H., Fu, Y., Hwang, S. J., Sigal, L., and Xue, X · 2022
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A new perspective on “how graph neural networks go beyond weisfeiler-lehman?”
Wijesinghe, A. and Wang, Q · 2022
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