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Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data.
A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2019b) · 1901
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Power up! robust graph convolutional network against evasion attacks based on graph powering
Jin, M., Chang, H., Zhu, W., and Sojoudi, S. (2019) · 1905
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El Ghaoui, L., Travacca, B., Gu, F., Tsai, A. Y.-T., and Askari, A. (2020) · 1908
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Fast and deep graph neural networks
Gallicchio, C. and Micheli, A. (2019) · 1911
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Unsupervised attributed multiplex network embedding
Park, C., Kim, D., Han, J., and Yu, H. (2019) · 1911
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Nonnegative matrices in the mathematical sciences
Berman, A. and Plemmons, R. J. (1994) · 1994
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The pagerank citation ranking: Bringing order to the web
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999) · 1999
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Diffusion-convolutional neural networks
Atwood, J. and Towsley, D. (2016) · 2001
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Spectral graph attention network
Chang, H., Rong, Y., Xu, T., Huang, W., Sojoudi, S., Huang, J., and Zhu, W. (2020) · 2003
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On graph kernels: Hardness results and efficient alternatives
Gärtner, T., Flach, P., and Wrobel, S. (2003) · 2003
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F. (2005) · 2005
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Simple and deep graph convolutional networks
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y. (2020) · 2007
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Efficient projections onto the l 1-ball for learning in high dimensions
Duchi, J., Shalev-Shwartz, S., Singer, Y., and Chandra, T. (2008) · 2008
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Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K. (2009) · 2009
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Graph echo state networks
Gallicchio, C. and Micheli, A. (2010) · 2010
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Libsvm: A library for support vector machines
Chang, C.-C. and Lin, C.-J. (2011) · 2011
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M. (2011) · 2011
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S. (2014) · 2014
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Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R. (2015) · 2015
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Deep graph kernels
Yanardag, P. and Vishwanathan, S. (2015) · 2015
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Defining and evaluating network communities based on ground-truth
Yang, J. and Leskovec, J. (2015) · 2015
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Discriminative embeddings of latent variable models for structured data
Dai, H., Dai, B., and Song, L. (2016) · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N. (2016) · 2016
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Networks
Newman, M. (2018) · 2018
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On the kronecker product
Schacke, K. (2018) · 2018
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Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D. (2018) · 2018
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018) · 2018
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Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018) · 2018
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An end-to-end deep learning architecture for graph classification
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2016) · 2016
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Propagation kernels: efficient graph kernels from propagated information
Neumann, M., Garnett, R., Bauckhage, C., and Kersting, K. (2016) · 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) · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J. (2017) · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017) · 2017
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Differentiable mpc for end-to-end planning and control
Amos, B., Jimenez, I., Sacks, J., Boots, B., and Kolter, J. Z. (2018) · 2018
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Zhang, M., Cui, Z., Neumann, M., and Chen, Y. (2018) · 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) · 2018
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Deep equilibrium models
Bai, S., Kolter, J. Z., and Koltun, V. (2019) · 2019
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Multi-dimensional graph convolutional networks
Ma, Y., Wang, S., Aggarwal, C. C., Yin, D., and Tang, J. (2019) · 2019
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Neodti: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
Wan, F., Hong, L., Xiao, A., Jiang, T., and Zeng, J. (2019) · 2019
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T. (2020) · 2020
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Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B. (2020) · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J. (2020) · 2020
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Deep learning on graphs: A survey
Zhang, Z., Cui, P., and Zhu, W. (2020) · 2020
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Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L. (2020) · 2020
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Heterogeneous graph attention network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., and Yu, P. S. (2019) · 2032
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