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In node classification tasks, graph convolutional neural networks (GCNs) have demonstrated competitive performance over traditional methods on diverse graph data.
Assortative mixing in networks
Newman, M. E · 2002
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Netprobe: a fast and scalable system for fraud detection in online auction networks
Pandit, S., Chau, D. H., Wang, S., and Faloutsos, C · 2007
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Social influence analysis in large-scale networks
Tang, J., Sun, J., Wang, C., and Yang, Z · 2009
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Query-driven active surveying for collective classification
Namata, G., London, B., Getoor, L., Huang, B., and EDU, U · 2012
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Linearized and single-pass belief propagation
Gatterbauer, W., Günnemann, S., Koutra, D., and Faloutsos, C · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Learning to represent programs with graphs, 2018
Allamanis, M., Brockschmidt, M., and Khademi, M · 2018
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Bojchevski, A. and Günnemann, S · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
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Few-shot learning with graph neural networks
Satorras, V. G. and Estrach, J. B · 2018
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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Encoding social information with graph convolutional networks forpolitical perspective detection in news media
Li, C. and Goldwasser, D · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Li, G., Muller, M., Thabet, A., and Ghanem, B · 2019
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2019
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Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L · 2019
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On proximity and structural role-based embeddings in networks: Misconceptions, techniques, and applications
Rossi, R. A., Jin, D., Kim, S., Ahmed, N. K., Koutra, D., and Lee, J. B · 2020
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Pointer graph networks
Veličković, P., Buesing, L., Overlan, M., Pascanu, R., Vinyals, O., and Blundell, C · 2020
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Neural execution engines: Learning to execute subroutines
Yan, Y., Swersky, K., Koutra, D., Ranganathan, P., and Hashemi, M · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., and Koutra, D · 2020
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Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2019
Cited alongside, same era.
Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2019
Cited alongside, same era.
Multi-scale attributed node embedding
Rozemberczki, B., Allen, C., and Sarkar, R · 2019
Cited alongside, same era.
Learning execution through neural code fusion
Shi, Z., Swersky, K., Tarlow, D., Ranganathan, P., and Hashemi, M · 2019
Cited alongside, same era.
Improving graph attention networks with large margin-based constraints
Wang, G., Ying, R., Huang, J., and Leskovec, J · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
Cited alongside, same era.
Baranwal, A., Fountoulakis, K., and Jagannath, A · 2021
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Beyond low-frequency information in graph convolutional networks
Bo, D., Wang, X., Shi, C., and Shen, H · 2021
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Adaptive universal generalized pagerank graph neural network, 2021
Chien, E., Peng, J., Li, P., and Milenkovic, O · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Lim, D., Hohne, F., Li, X., Huang, S. L., Gupta, V., Bhalerao, O., and Lim, S. N · 2021
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Is heterophily a real nightmare for graph neural networks to do node classification?
Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.-W., and Precup, D · 2021
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Is homophily a necessity for graph neural networks?
Ma, Y., Liu, X., Shah, N., and Tang, J · 2021
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Graph neural networks with heterophily
Zhu, J., Rossi, R. A., Rao, A., Mai, T., Lipka, N., Ahmed, N. K., and Koutra, D · 2021
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Memory-based message passing: Decoupling the message for propagation from discrimination
Chen, J., Liu, W., and Pu, J · 2022
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