2018

Deep Learning on Graphs: A Survey

Zhang, Ziwei, Cui, Peng, Zhu, Wenwu

Understand

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing.

However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs.
Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques.
In this survey, we comprehensively review the different types of deep learning methods on graphs.

Built on

S. Kullback and R. A. Leibler, “On information and sufficiency,” The annals of mathematical statistics , 1951
1951
Earlier work this paper cites.
M. J. Powell, “An efficient method for finding the minimum of a function of several variables without calculating derivatives,” The computer journal , 1964
1964
Earlier work this paper cites.
J. MacQueen et al. , “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability , 1967
1967
Earlier work this paper cites.
S. Vallender, “Calculation of the wasserstein distance between probability distributions on the line,” Theory of Probability & Its Applications , 1974
1974
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature , 1986
1986
Earlier work this paper cites.
L. B. Almeida, “A learning rule for asynchronous perceptrons with feedback in a combinatorial environment.” in Proceedings, 1st First International Conference on Neural Networks , 1987
1987
Earlier work this paper cites.
F. J. Pineda, “Generalization of back-propagation to recurrent neural networks,” Physical Review Letters , 1987
1987
Earlier work this paper cites.
L. Lovász et al. , “Random walks on graphs: A survey,” Combinatorics , 1993
1993
Earlier work this paper cites.
G. Klir and B. Yuan, Fuzzy sets and fuzzy logic . Prentice hall New Jersey, 1995
1995
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , 1997
1997
Earlier work this paper cites.
P. Frasconi, M. Gori, and A. Sperduti, “A general framework for adaptive processing of data structures,” IEEE transactions on Neural Networks , 1998
1998
Earlier work this paper cites.
A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science , 1999
1999
Earlier work this paper cites.
L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999
1999
Earlier work this paper cites.
R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in Neural Information Processing systems , 2000
2000
Earlier work this paper cites.
M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Advances in neural information processing systems , 2002
2002
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in IEEE International Joint Conference on Neural Networks Proceedings , 2005
2005
Earlier work this paper cites.
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang, “A tutorial on energy-based learning,” Predicting structured data , 2006
2006
Earlier work this paper cites.
S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: A general framework for dimensionality reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2007
2007
Earlier work this paper cites.
U. Von Luxburg, “A tutorial on spectral clustering,” Statistics and computing , 2007
2007
Earlier work this paper cites.
I. S. Dhillon, Y. Guan, and B. Kulis, “Weighted graph cuts without eigenvectors a multilevel approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2007
2007
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in International Conference on Machine Learning , 2008
2008
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , 2008
2008
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , 2009
2009
Earlier work this paper cites.
D. Ruppert, “The elements of statistical learning: Data mining, inference, and prediction,” Journal of the Royal Statistical Society , 2010
2010
Earlier work this paper cites.
M. A. Khamsi and W. A. Kirk, An introduction to metric spaces and fixed point theory . John Wiley & Sons, 2011
2011
Earlier work this paper cites.
D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,” Applied and Computational Harmonic Analysis , 2011
2011
Earlier work this paper cites.
N. Shervashidze, P. Schweitzer, E. J. v. Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels,” Journal of Machine Learning Research , 2011
2011
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Processing Magazine , 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , 2012
2012
Earlier work this paper cites.
J. Leskovec and J. J. Mcauley, “Learning to discover social circles in ego networks,” in NeurIPS , 2012
2012
Earlier work this paper cites.
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains,” IEEE Signal Processing Magazine , 2013
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 3rd International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research , 2014
2014
Earlier work this paper cites.
K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder–decoder for statistical machine translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing , 2014
2014
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, “Spectral networks and locally connected networks on graphs,” in Proceedings of the 3rd International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
O. Levy and Y. Goldberg, “Neural word embedding as implicit matrix factorization,” in Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
B. D. Mckay and A. Piperno, Practical graph isomorphism, II . Academic Press, Inc., 2014
2014
Earlier work this paper cites.
F. Tian, B. Gao, Q. Cui, E. Chen, and T.-Y. Liu, “Learning deep representations for graph clustering.” in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence , 2014
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proceedings of the 3rd International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining , 2014
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , 2015
2015
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proceedings of the 4th International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
M. Brockschmidt, Y. Chen, B. Cook, P. Kohli, and D. Tarlow, “Learning to decipher the heap for program verification,” in Workshop on Constructive Machine Learning at the International Conference on Machine Learning , 2015
2015
Earlier work this paper cites.
M. Henaff, J. Bruna, and Y. LeCun, “Deep convolutional networks on graph-structured data,” arXiv preprint arXiv:1506.05163 , 2015
Original
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in Neural Information Processing Systems , 2015
2015
Earlier work this paper cites.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th International Conference on World Wide Web , 2015
2015
Earlier work this paper cites.
S. Chang, W. Han, J. Tang, G.-J. Qi, C. C. Aggarwal, and T. S. Huang, “Heterogeneous network embedding via deep architectures,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2015
2015
Earlier work this paper cites.
A.-L. Barabasi, Network science . Cambridge university press, 2016
2016
Earlier work this paper cites.
C. Zang, P. Cui, and C. Faloutsos, “Beyond sigmoids: The nettide model for social network growth, and its applications,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2016
2016
Earlier work this paper cites.
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” in Proceedings of the 5th International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in Neural Information Processing Systems , 2016
2016
Earlier work this paper cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in International Conference on Machine Learning , 2016
2016
Earlier work this paper cites.
J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in Advances in Neural Information Processing Systems , 2016
2016
Earlier work this paper cites.
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, “Molecular graph convolutions: moving beyond fingerprints,” Journal of Computer-Aided Molecular Design , 2016
2016
Earlier work this paper cites.
L. Babai, “Graph isomorphism in quasipolynomial time,” in Proceedings of the forty-eighth annual ACM symposium on Theory of Computing , 2016
2016
Earlier work this paper cites.
D. I. Shuman, M. J. Faraji, and P. Vandergheynst, “A multiscale pyramid transform for graph signals,” IEEE Transactions on Signal Processing , 2016
2016
Earlier work this paper cites.

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2016
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D. Wang, P. Cui, and W. Zhu, “Structural deep network embedding,” in KDD , 2016
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S. Cao, W. Lu, and Q. Xu, “Deep neural networks for learning graph representations.” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence , 2016
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T. N. Kipf and M. Welling, “Variational graph auto-encoders,” NIPS Workshop on Bayesian Deep Learning , 2016
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2016
Cited alongside, same era.
A. Jain, A. R. Zamir, S. Savarese, and A. Saxena, “Structural-rnn: Deep learning on spatio-temporal graphs,” in Computer Vision and Pattern Recognition , 2016
2016
Cited alongside, same era.
S. Sukhbaatar, R. Fergus et al. , “Learning multiagent communication with backpropagation,” in Advances in Neural Information Processing Systems , 2016
2016
Cited alongside, same era.
P. W. Battaglia, R. Pascanu, M. Lai, D. Rezende, and K. Kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” in Advances in Neural Information Processing Systems , 2016
2016
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2016
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Then

J. Ma, P. Cui, and W. Zhu, “Depthlgp: Learning embeddings of out-of-sample nodes in dynamic networks,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence , 2018
2018
Closest in time.
F. Scarselli, A. C. Tsoi, and M. Hagenbuchner, “The vapnik–chervonenkis dimension of graph and recursive neural networks,” Neural Networks , 2018
2018
Closest in time.
R. v. d. Berg, T. N. Kipf, and M. Welling, “Graph convolutional matrix completion,” KDD’18 Deep Learning Day , 2018
2018
Closest in time.
K. Tu, P. Cui, X. Wang, P. S. Yu, and W. Zhu, “Deep recursive network embedding with regular equivalence,” in KDD , 2018
2018
Closest in time.
A. Bojchevski and S. Günnemann, “Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking,” in Proceedings of the 7th International Conference on Learning Representations , 2018
2018
Closest in time.
D. Zhu, P. Cui, D. Wang, and W. Zhu, “Deep variational network embedding in wasserstein space,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018
2018
Closest in time.
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang, “Adversarially regularized graph autoencoder for graph embedding.” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence , 2018
2018
Closest in time.
W. Yu, C. Zheng, W. Cheng, C. C. Aggarwal, D. Song, B. Zong, H. Chen, and W. Wang, “Learning deep network representations with adversarially regularized autoencoders,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018
2018
Closest in time.
Z. Zhang, “A note on spectral clustering and svd of graph data,” arXiv preprint arXiv:1809.11029 , 2018
Original
2018
Closest in time.
J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec, “Graph convolutional policy network for goal-directed molecular graph generation,” in Advances in Neural Information Processing Systems , 2018
2018
Closest in time.
N. De Cao and T. Kipf, “MolGAN: An implicit generative model for small molecular graphs,” ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models , 2018
2018
Closest in time.
J. B. Lee, R. Rossi, and X. Kong, “Graph classification using structural attention,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018
2018
Closest in time.
R. Das, S. Dhuliawala, M. Zaheer, L. Vilnis, I. Durugkar, A. Krishnamurthy, A. Smola, and A. McCallum, “Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,” in Proceedings of the 7th International Conference on Learning Representations , 2018
2018
Closest in time.
H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo, “Graphgan: Graph representation learning with generative adversarial nets,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Closest in time.
Q. Dai, Q. Li, J. Tang, and D. Wang, “Adversarial network embedding,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Closest in time.
M. Ding, J. Tang, and J. Zhang, “Semi-supervised learning on graphs with generative adversarial nets,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management , 2018
2018
Closest in time.
A. Bojchevski, O. Shchur, D. Zügner, and S. Günnemann, “Netgan: Generating graphs via random walks,” in International Conference on Machine Learning , 2018
2018
Closest in time.
D. Zügner, A. Akbarnejad, and S. Günnemann, “Adversarial attacks on neural networks for graph data,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018
2018
Closest in time.
H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song, “Adversarial attack on graph structured data,” in Proceedings of the 35th International Conference on Machine Learning , 2018
2018
Closest in time.
J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang, “Deepinf: Modeling influence locality in large social networks,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2018
2018
Closest in time.
T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Physical Review Letters , 2018
2018
Closest in time.
M. Zitnik, M. Agrawal, and J. Leskovec, “Modeling polypharmacy side effects with graph convolutional networks,” Bioinformatics , 2018
2018
Closest in time.
F. Dutil, J. P. Cohen, M. Weiss, G. Derevyanko, and Y. Bengio, “Towards gene expression convolutions using gene interaction graphs,” in International Conference on Machine Learning Workshop on Computational Biology , 2018
2018
Closest in time.
V. Garcia and J. Bruna, “Few-shot learning with graph neural networks,” in Proceedings of the 7th International Conference on Learning Representations , 2018
2018
Closest in time.
S. Qi, W. Wang, B. Jia, J. Shen, and S.-C. Zhu, “Learning human-object interactions by graph parsing neural networks,” in Proceedings of the European Conference on Computer Vision , 2018
2018
Closest in time.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence , 2018
2018
Closest in time.
Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in Proceedings of the 7th International Conference on Learning Representations , 2018
2018
Closest in time.
M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” in Proceedings of the 7th International Conference on Learning Representations , 2018
2018
Closest in time.
Z. Li, Q. Chen, and V. Koltun, “Combinatorial optimization with graph convolutional networks and guided tree search,” in NeurIPS , 2018
2018
Closest in time.
T. Derr, Y. Ma, and J. Tang, “Signed graph convolutional network,” in Data Mining, 2018 IEEE International Conference on , 2018
2018
Closest in time.
K. Tu, P. Cui, X. Wang, F. Wang, and W. Zhu, “Structural deep embedding for hyper-networks,” in AAAI , 2018
2018
Closest in time.
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, “Pitfalls of graph neural network evaluation,” Relational Representation Learning Workshop, NeurIPS 2018 , 2018
2018
Closest in time.
Y. Li, O. Vinyals, C. Dyer, R. Pascanu, and P. Battaglia, “Learning deep generative models of graphs,” arXiv preprint arXiv:1803.03324 , 2018
Original
2018
Closest in time.
J. B. Lee, R. A. Rossi, S. Kim, N. K. Ahmed, and E. Koh, “Attention models in graphs: A survey,” ACM Transactions on Knowledge Discovery from Data , 2019
2019
Closest in time.
S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: Algorithms, applications and open challenges,” in International Conference on Computational Social Networks , 2019
2019
Closest in time.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” arXiv preprint arXiv:1901.00596 , 2019
Original
2019
Closest in time.
B. Xu, H. Shen, Q. Cao, Y. Qiu, and X. Cheng, “Graph wavelet neural network,” in Proceedings of the 8th International Conference on Learning Representations , 2019
2019
Closest in time.
X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu, “Heterogeneous graph attention network,” in The World Wide Web Conference , 2019
2019
Closest in time.
J. Klicpera, A. Bojchevski, and S. Günnemann, “Predict then propagate: Graph neural networks meet personalized pagerank,” in Proceedings of the 8th International Conference on Learning Representations , 2019
2019
Closest in time.
Z. Chen, L. Li, and J. Bruna, “Supervised community detection with line graph neural networks,” in Proceedings of the 8th International Conference on Learning Representations , 2019
2019
Closest in time.
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in International Conference on Machine Learning , 2019
2019
Closest in time.
T. Maehara, “Revisiting graph neural networks: All we have is low-pass filters,” arXiv preprint arXiv:1905.09550 , 2019
Original
2019
Closest in time.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in Proceedings of the 8th International Conference on Learning Representations , 2019
2019
Closest in time.
P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in Proceedings of the 8th International Conference on Learning Representations , 2019
2019
Closest in time.
S. Verma and Z.-L. Zhang, “Stability and generalization of graph convolutional neural networks,” in KDD , 2019
2019
Closest in time.
K. Do, T. Tran, and S. Venkatesh, “Graph transformation policy network for chemical reaction prediction,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019
2019
Closest in time.
D. Zügner and S. Günnemann, “Adversarial attacks on graph neural networks via meta learning,” in Proceedings of the 8th International Conference on Learning Representations , 2019
2019
Closest in time.
J. Ma, C. Zhou, P. Cui, H. Yang, and W. Zhu, “Learning disentangled representations for recommendation,” in Advances in Neural Information Processing Systems , 2019
2019
Closest in time.
M. Prates, P. H. Avelar, H. Lemos, L. C. Lamb, and M. Y. Vardi, “Learning to solve np-complete problems: A graph neural network for decision tsp,” in AAAI , 2019
2019
Closest in time.
K. Tu, J. Ma, P. Cui, J. Pei, and W. Zhu, “Autone: Hyperparameter optimization for massive network embedding,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019
2019
Closest in time.
R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “Gnn explainer: A tool for post-hoc explanation of graph neural networks,” in Advances in Neural Information Processing Systems , 2019
2019
Closest in time.
D. Zhu, Z. Zhang, P. Cui, and W. Zhu, “Robust graph convolutional networks against adversarial attacks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019
2019
Closest in time.
M. Jin, H. Chang, W. Zhu, and S. Sojoudi, “Power up! robust graph convolutional network against evasion attacks based on graph powering,” arXiv preprint arXiv:1905.10029 , 2019
Original
2019
Closest in time.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
Closest in time.
M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, Z. Huang, Q. Guo, H. Zhang, H. Lin, J. Zhao, J. Li, A. J. Smola, and Z. Zhang, “Deep graph library: Towards efficient and scalable deep learning on graphs,” ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
Closest in time.
R. Zhu, K. Zhao, H. Yang, W. Lin, C. Zhou, B. Ai, Y. Li, and J. Zhou, “Aligraph: A comprehensive graph neural network platform,” in Proceedings of the 45th International Conference on Very Large Data Bases , 2019
2019
Closest in time.

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