Fetching the paper…
Reading the bibliography…
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data.
J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in Proc. of NIPS , 2016, pp. 1993–2001
2001
Earlier work this paper cites.
R. R. Coifman and M. Maggioni, “Diffusion wavelets,” Applied and Computational Harmonic Analysis , vol. 21, no. 1, pp. 53–94, 2006
2006
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 , vol. 20, no. 1, pp. 61–80, 2009
2009
Earlier work this paper cites.
A. Micheli, “Neural network for graphs: A contextual constructive approach,” IEEE Transactions on Neural Networks , vol. 20, no. 3, pp. 498–511, 2009
2009
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 , vol. 30, no. 2, pp. 129–150, 2011
2011
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 , vol. 30, no. 3, pp. 83–98, 2013
2013
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 2nd International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” in Proceedings of the 3rd International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Proceedings of the 30th Conference on Neural Information Processing Systems , 2016, pp. 3844–3852
2016
Earlier work this paper cites.
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine , vol. 34, no. 4, pp. 18–42, 2017
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the 34th International Conference on Machine Learning , 2017, pp. 1263–1272
2017
Earlier work this paper cites.
C. Wang, S. Pan, G. Long, X. Zhu, and J. Jiang, “Mgae: Marginalized graph autoencoder for graph clustering,” in Proceedings of the 26th ACM International Conference on Information and Knowledge Management . ACM, 2017, pp. 889–898
2017
Earlier work this paper cites.
D. Marcheggiani and I. Titov, “Encoding sentences with graph convolutional networks for semantic role labeling,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 1506–1515
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the 5th International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model cnns,” in Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2017
2017
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in Proceedings of the 5th International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st Conference on Neural Information Processing Systems , 2017, pp. 1024–1034
2017
Earlier work this paper cites.
M. Zhang and Y. Chen, “Link prediction based on graph neural networks,” in Proceedings of the 32nd Conference on Neural Information Processing Systems , 2018
2018
Earlier work this paper cites.
M. Simonovsky and N. Komodakis, “Graphvae: Towards generation of small graphs using variational autoencoders,” in Proceedings of the 27th International Conference on Artificial Neural Networks . Springer, 2018, pp. 412–422
2018
Earlier work this paper cites.
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2018, pp. 974–983
2018
Earlier work this paper cites.
Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2018, p. 753–763
2018
Cited alongside, same era.
R. Li, S. Wang, F. Zhu, and J. Huang, “Adaptive graph convolutional neural networks,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence , 2018, pp. 3546–3553
2018
Cited alongside, same era.
C. Zhuang and Q. Ma, “Dual graph convolutional networks for graph-based semi-supervised classification,” in Proceedings of the Web Conference , 2018, pp. 499–508
2018
Cited alongside, same era.
R. Levie, F. Monti, X. Bresson, and M. M. Bronstein, “Cayleynets: Graph convolutional neural networks with complex rational spectral filters,” IEEE Transactions on Signal Processing , vol. 67, no. 1, pp. 97–109, 2018
2018
Cited alongside, same era.
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,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
Later among the works it cites.
C. Zhang, D. Song, C. Huang, A. Swami, and N. V. Chawla, “Heterogeneous graph neural network,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 793–803
2019
Later among the works it cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Later among the works it cites.
Z. Zhang, P. Cui, and W. Zhu, “Deep learning on graphs: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Dai, Z. Kozareva, B. Dai, A. Smola, and L. Song, “Learning steady-states of iterative algorithms over graphs,” in Proceedings of the International Conference on Machine Learning , 2018, pp. 1114–1122
2018
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph Attention Networks,” in Proceedings of the 6th International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” in Proceedings of the 35th International Conference on Machine Learning , 2018
2018
Cited alongside, same era.
Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence , 2018
2018
Cited alongside, same era.
S. Pan, R. Hu, S.-f. Fung, G. Long, J. Jiang, and C. Zhang, “Learning graph embedding with adversarial training methods,” IEEE Transactions on Cybernetics , vol. 50, no. 6, pp. 2475–2487, 2019
2019
Cited alongside, same era.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” ACM Transactions on Graphics (TOG) , 2019
2019
Cited alongside, same era.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019
2019
Cited alongside, same era.
F. Wu, T. Zhang, A. H. d. Souza Jr, C. Fifty, T. Yu, and K. Q. Weinberger, “Simplifying graph convolutional networks,” in Proceedings of the 36th International Conference on Machine Learning , 2019
2019
Cited alongside, same era.
H. Wang, C. Zhou, X. Chen, J. Wu, S. Pan, and J. Wang, “Graph stochastic neural networks for semi-supervised learning,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
M. Wu, S. Pan, and X. Zhu, “Openwgl: Open-world graph learning,” in Proc. Of the 20th IEEE International Conference on Data Mining, November 17-20, 2020, Sorrento, Italy , 2020
2020
Later among the works it cites.
S. Zhu, S. Pan, C. Zhou, J. Wu, Y. Cao, and B. Wang, “Graph geometry interaction learning,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, “Simple and deep graph convolutional networks,” in International Conference on Machine Learning . PMLR, 2020, pp. 1725–1735
2020
Later among the works it cites.
2020
Later among the works it cites.
I. Spinelli, S. Scardapane, and A. Uncini, “Adaptive propagation graph convolutional network,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Later among the works it cites.
X. Fu, J. Zhang, Z. Meng, and I. King, “Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding,” in Proceedings of The Web Conference 2020 , 2020, pp. 2331–2341
2020
Later among the works it cites.
2021
Closest in time.
H. Zeng, M. Zhang, Y. Xia, A. Srivastava, A. Malevich, R. Kannan, V. Prasanna, L. Jin, and R. Chen, “Decoupling the depth and scope of graph neural networks,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Closest in time.
2021
Closest in time.
Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and P. S. Yu, “Graph self-supervised learning: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
Closest in time.
X. Zheng, Y. Liu, S. Pan, M. Zhang, D. Jin, and P. S. Yu, “Graph neural networks for graphs with heterophily: A survey,” arXiv 2202.07082 , 2022
2022
Closest in time.
2022
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, pp. 2022–2032
2032
Closest in time.