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We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these methods to a wider variety of problems.
On a test of whether one of two random variables is stochastically larger than the other
Mann, H. B. and D. R. Whitney 1947 · 1947
Earlier work this paper cites.
On the efficient classification of data structures by neural networks
Frasconi, P., V. D. S. Marta, M. Gori, V. Roma, and A. Sperduti 1997 · 1997
Earlier work this paper cites.
Supervised neural networks for the classification of structures
Sperduti, A. and A. Starita 1997 · 1997
Earlier work this paper cites.
Exact and approximate graph matching using random walks
Gori, M., M. Maggini, and L. Sarti 2005 · 2005
Earlier work this paper cites.
Diffusion-driven multiscale analysis on manifolds and graphs: top-down and bottom-up constructions
Szlam, A. D., M. Maggioni, R. R. Coifman, and J. C. BremerJr. 2005 · 2005
Earlier work this paper cites.
The graph neural network model
Scarselli, F., M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini 2009 · 2009
Earlier work this paper cites.
Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi-Supervised Learning
Gavish, M., B. Nadler, R. R. Coifman, and N. Haven 2010 · 2010
Earlier work this paper cites.
Weisfeiler-Lehman Graph Kernels
Shervashidze, N., P. Schweitzer, E. Jan van Leeuwen, K. Mehlhorn, and K. Borgwardt 2011 · 2011
Earlier work this paper cites.
Unsupervised generation of data mining features from linked open data
Paulheim, H. and J. Fümkranz 2012 · 2012
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
Bergstra, J., D. Yamins, and D. Cox 2013 · 2013
Earlier work this paper cites.
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., W. Zaremba, A. Szlam, and Y. LeCun 2013 · 2013
Earlier work this paper cites.
Wavelets on graphs via deep learning
Rustamov, R. and L. Guibas 2013 · 2013
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
Karpathy, A., G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and F. F. Li 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and J. Ba 2014 · 2014
Cited alongside, same era.
Substructure counting graph kernels for machine learning from rdf data
de Vries, G. K. D. and S. de Rooij 2015 · 2015
Cited alongside, same era.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams 2015 · 2015
Cited alongside, same era.
Deep Convolutional Networks on Graph-Structured Data
Henaff, M., J. Bruna, and Y. LeCun 2015 · 2015
Cited alongside, same era.
Massively Multitask Networks for Drug Discovery
Ramsundar, B., S. Kearnes, P. Riley, D. Webster, D. Konerding, and V. Pande 2015 · 2015
Cited alongside, same era.
Low Data Drug Discovery with One-shot Learning
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., J. Bruna, Y. Lecun, A. Szlam, and P. Vandergheynst 2017 · 2017
Later among the works it cites.
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., L. A. Hendricks, M. Rohrbach, S. Venugopalan, S. Guadarrama, K. Saenko, and T. Darrell 2017 · 2017
Later among the works it cites.
Geometric deep learning on graphs and manifolds using mixture model CNNs
Monti, F., D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein 2017 · 2017
Later among the works it cites.
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin 2017 · 2017
Later among the works it cites.
Graph Attention Networks
Veličković, P., G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio 2017 · 2017
Later among the works it cites.
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Diffusion-Convolutional Neural Networks
Atwood, J. and D. Towsley 2016 · 2016
Cited alongside, same era.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M., X. Bresson, and P. Vandergheynst 2016 · 2016
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., K. McCloskey, M. Berndl, V. Pande, and P. Riley 2016 · 2016
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. and M. Welling 2016 · 2016
Cited alongside, same era.
RDF2Vec: RDF graph embeddings for data mining
Ristoski, P. and H. Paulheim 2016 · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals 2016 · 2016
Cited alongside, same era.
Adaptive Edge Features Guided Graph Attention Networks
Gong, L. and Q. Cheng 2018 · 2018
Later among the works it cites.
Attention Models in Graphs: A Survey
Lee, J. B., R. A. Rossi, S. Kim, N. K. Ahmed, and E. Koh 2018 · 2018
Later among the works it cites.
Dual-Primal Graph Convolutional Networks
Monti, F., O. Shchur, A. Bojchevski, O. Litany, S. Günnemann, and M. M. Bronstein 2018 · 2018
Later among the works it cites.
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling 2018 · 2018
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Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon 2018 · 2018
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MoleculeNet: A benchmark for molecular machine learning
Wu, Z., B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande 2018 · 2018
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GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Zhang, J., X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung 2018 · 2018
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