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Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data.
Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems
Spielman, D. A. and Teng, S.-H · 2004
Earlier work this paper cites.
Graph sparsification by effective resistances
Spielman, D. A. and Srivastava, N · 2011
Earlier work this paper cites.
Spectral sparsification of graphs
Spielman, D. A. and Teng, S.-H · 2011
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Earlier work this paper cites.
An sdp-based algorithm for linear-sized spectral sparsification
Lee, Y. T. and Sun, H · 2017
Earlier work this paper cites.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. and Grossman, J. C · 2018
Cited alongside, same era.
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.
Network representation learning: Consolidation and renewed bearing
Gurukar, S., Vijayan, P., Srinivasan, A., Bajaj, G., Cai, C., Keymanesh, M., Kumar, S., Maneriker, P., Mitra, A., Patel, V., et al · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Li, G., Müller, M., Thabet, A., and Ghanem, B · 2019
Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2019
Later among the works it cites.
Gmnn: Graph markov neural networks
Qu, M., Bengio, Y., and Tang, J · 2019
Later among the works it cites.
Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2019
Later among the works it cites.
Simplifying graph convolutional networks
Wu, F., Zhang, T., Souza Jr, A. H. d., Fifty, C., Yu, T., and Weinberger, K. Q · 2019
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Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L · 2019
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Cited alongside, same era.
Break the ceiling: Stronger multi-scale deep graph convolutional networks
Luan, S., Zhao, M., Chang, X.-W., and Precup, D · 2019
Cited alongside, same era.
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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