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We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Weisfeiler, B. and Lehman, A. A · 1968
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K · 1991
Earlier work this paper cites.
A fast and high quality multilevel scheme for partitioning irregular graphs
Karypis, G. and Kumar, V · 1998
Earlier work this paper cites.
The tradeoffs of large scale learning
Bottoue, L. and Bousquet, O · 2007
Earlier work this paper cites.
Weighted graph cuts without eigenvectors: A multilevel approach
Dhillon, I. S., Guan, Y., and Kulis, B · 2007
Earlier work this paper cites.
Collective classification in network data
Sen, G., Namata, G., Bilgic, M., and Getoor, L · 2008
Earlier work this paper cites.
Deep graph neural networks with shallow subgraph samplers
Zeng, H., Zhang, M., Xia, Y., Srivastava, A., Kannan, R., Prasanna, V., Jin, L., Malevich, A., and Chen, R · 2012
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhutdinov, R · 2016
Earlier work this paper cites.
Gemini: A computation-centric distributed graph processing system
Zhu, X., Chen, W., Zheng, W., and Ma, X · 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.
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.
PointNet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C. R., Yi, L., Su, H., and Guibas, L. J · 2017
Earlier work this paper cites.
Deep sets
Zaheer, M., Kottur, S., Ravanbhakhsh, S., Póczos, B., Salakhutdinov, R., and Smola, A. J · 2017
Earlier work this paper cites.
Adaptive sampling towards fast graph representation learning
Huang, W., Zhang, T., Rong, Y., and Huang, J · 2018
Earlier work this paper cites.
Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
Earlier work this paper cites.
Towards robust neural networks with lipschitz continuity
Usama, M. and Chang, D. E · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., and Jegelka, S · 2018
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.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
Cited alongside, same era.
NeuGraph: Parallel deep neural network computation on large graphs
Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., and Bresson, X · 2020
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SIGN: Scalable inception graph neural networks
Frasca, F., Rossi, E., Eynard, D., Chamberlain, B., Bronstein, M. M., and Monti, F · 2020
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Graph representation learning
Hamilton, W. L · 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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Deep Learning on Graphs
Ma, Y. and Tang, J · 2020
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Ma, L., Yang, Z., Miao, Y., Xue, J., Wu, M., Zhou, L., and Dai, Y · 2019
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Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
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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
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Discriminative structural graph classification
Seo, Y., Loukas, A., and Perraudin, N · 2019
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Dynamic graph CNN for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2019
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Simplifying graph convolutional networks
Wu, F., Zhang, T., de Souza Jr., A. H., Fifty, C., Yu, T., and Weinberger, K. Q · 2019
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Wiki-CS: A wikipedia-based benchmark for graph neural networks
Mernyei, P. and Cangea, C · 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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Masked label prediction: Unified message passing model for semi-supervised classification
Shi, Y., Huang, Z., Wang, W., Zhong, H., Feng, S., and Sun, Y · 2020
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Reducing communcation in graph neural network training
Tripathy, A., Yelick, K., and Buluc, A · 2020
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Don’t stack layers in graph neural networks, wire them randomly
Valsesia, D., Fracastoro, G., and Magli, E · 2020
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BDS-GCN: Efficient full-graph training of graph convolutional nets with partition-parallelism and boundary sampling
Wan, C., Li, Y., Kim, N. S., and Lin, Y · 2020
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L 2 -GCN: Layer-wise and learned efficient training of graph convolutional networks
You, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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Scalable graph neural networks for heterogeneous graphs
Yu, L., Shen, J., Li, J., and Lerer, A · 2020
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DistDGL: Distributed graph neural network for training for billion-scale graphs
Zheng, D., Ma, C., Wang, M., Zhou, J., Su, Q., Song, X., Gan, Q., Zhang, Z., and Karypis, G · 2020
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Combining label propagation and simple models out-performs graph neural networks
Huang, Q., He, H., Singh, A., Lim, S. N., and Benson, A. R · 2021
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Graph traversal with tensor functionals: A meta-algorithm for scalable learning
Markowitz, E., Balasubramanian, K., Mirtaheri, M., Abu-El-Haija, S., Perozzi, B., Ver Steeg, G., and Galstyan, A · 2021
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