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Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers.
Singularity: Scientific containers for mobility of compute
Kurtzer, G. M., Sochat, V., and Bauer, M. W · 1932
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S. V. N., Smola, A. J., and Kriegel, H.-P · 2005
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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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Query-driven Active Surveying for Collective Classification
Namata, G., London, B., Getoor, L., and Huang, B · 2012
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
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Deep Graph Kernels
Yanardag, P. and Vishwanathan, S. V. N · 2015
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Benchmark data sets for graph kernels, 2016
Kersting, K., Kriege, N. M., Morris, C., Mutzel, P., and Neumann, M · 2016
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2017
Cited alongside, same era.
Towards Sparse Hierarchical Graph Classifiers
Cangea, C., Veličković, P., Jovanović, N., Kipf, T., and Liò, P · 2018
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Graph U-Net
Gao, H. and Ji, S · 2018
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Pitfalls of Graph Neural Network Evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
Later among the works it cites.
Hierarchical graph representation learning with differentiable pooling
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Lee, J., Lee, I., and Kang, J · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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