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Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships.
Semi-supervised learning using gaussian fields and harmonic functions
Zhu, X., Ghahramani, Z., and Lafferty, J · 1905
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Two-dimensional spectral analysis of cortical receptive field profiles
Daugman, J · 1980
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Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters
Daugman, J · 1985
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Learning task-dependent distributed representations by backpropagation through structure
Goller, C. and Kuchler, A · 1996
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Link-based classification
Lu, Q. and Getoor, L · 2003
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Belkin, M., Niyogi, P., and Sindhwani, V · 2006
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Deeplearning via semi-supervised embedding
Weston, J., Ratle, F., Mobahi, H., and Collobert, R · 2012
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 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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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems
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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Visibility of minorities in social networks
Karimi, F., Genois, M., Wagner, C., Singer, P., and Strohmaier, M · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. and Welling, M · 2017
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N-gcn: Multi-scale graph convolution for semi-supervised node classification
Abu-El-Haija, S., Kapoor, A., Perozzi, B., and Lee, J · 2018
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Gordon, A., Eban, E., Nachum, O., Chen, B., Wu, H., Yang, T.-J., and Choi, E · 2018
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Higher-order graph convolutional networks
Lee, J. B., Rossi, R. A., Kong, X., Kim, S., Koh, E., and Rao, A · 2018
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Abadi, M., Agarwal, A., et al · 2016
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Diffusion-convolutional neural networks
Atwood, J. and Towsley, D · 2016
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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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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhutdinov, R · 2016
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Discovering communities and anomalies in attributed graphs: Interactive visual exploration and summarization
Perozzi, B. and Akoglu, L · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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