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Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Natural image statistics and neural representation
Simoncelli, E. P. and Olshausen, B. A · 2001
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A genomic regulatory network for development
Davidson, E. H., Rast, J. P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., et al · 2002
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Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D. and Doig, A. J · 2003
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S., Smola, A. J., and Kriegel, H.-P · 2005
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Weighted graph cuts without eigenvectors a multilevel approach
Dhillon, I. S., Guan, Y., and Kulis, B · 2007
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Comparison of descriptor spaces for chemical compound retrieval and classification
Wale, N., Watson, I. A., and Karypis, G · 2008
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Life in the network: the coming age of computational social science
Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al · 2009
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Fast neighborhood subgraph pairwise distance kernel
Costa, F. and Grave, K. D · 2010
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Leeuwen, E. J. v., Mehlhorn, K., and Borgwardt, K. M · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2014
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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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Large-scale video classification with convolutional neural networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Cited alongside, same era.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Cited alongside, same era.
Graph invariant kernels
Orsini, F., Frasconi, P., and De Raedt, L · 2015
Cited alongside, same era.
Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M · 2015
Cited alongside, same era.
Long short-term memory-networks for machine reading
Cheng, J., Dong, L., and Lapata, M · 2016
Graph convolutional matrix completion
van den Berg, R., Kipf, T. N., and Welling, M · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Towards sparse hierarchical graph classifiers
Cangea, C., Veličković, P., Jovanović, N., Kipf, T., and Liò, P · 2018
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Troubling trends in machine learning scholarship
Lipton, Z. C. and Steinhardt, J · 2018
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2018
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Cited alongside, same era.
Discriminative embeddings of latent variable models for structured data
Dai, H., Dai, B., and Song, L · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Benchmark data sets for graph kernels, 2016
Kersting, K., Kriege, N. M., Morris, C., Mutzel, P., and Neumann, M · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Cited alongside, same era.
A decomposable attention model for natural language inference
Parikh, A., Täckström, O., Das, D., and Uszkoreit, J · 2016
Cited alongside, same era.
Large-scale hierarchical text classification with recursively regularized deep graph-cnn
Peng, H., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., Song, Y., and Yang, Q · 2018
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Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification
Rhee, S., Seo, S., and Kim, S · 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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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Convolutional geometric matrix completion
Yao, K.-L. and Li, W.-J · 2018
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Graph convolutional networks for text classification
Yao, L., Mao, C., and Luo, Y · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W. L., and Leskovec, J · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J · 2018
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Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., and Sun, M · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Zitnik, M., Agrawal, M., 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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Graph u-net
Gao, H. and Ji, S · 2019
Closest in time.