Fetching the paper…
Reading the bibliography…
Investigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
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 , vol. 29, no. 3, pp. 93–93, 2008
2008
Earlier work this paper cites.
J. Tang, J. Sun, C. Wang, and Z. Yang, “Social influence analysis in large-scale networks,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining , 2009, pp. 807–816
2009
Earlier work this paper cites.
S. Ji, W. Xu, M. Yang, and K. Yu, “3d convolutional neural networks for human action recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 35, no. 1, pp. 221–231, 2012
2012
Earlier work this paper cites.
2014
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.
J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel, “Image-based recommendations on styles and substitutes,” in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2015, pp. 43–52
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308 , 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
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, pp. 3844–3852
2016
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-Volume 70 . JMLR. org, 2017, pp. 1263–1272
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in Neural Information Processing Systems , 2017, pp. 1024–1034
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the International Conference on Learning Representations , 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in Proceedings of the International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” in AAAI , 2018, pp. 4438–4445
2018
Cited alongside, same era.
2019
Later among the works it cites.
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
Later among the works it cites.
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in International Conference on Machine Learning , 2019, pp. 6861–6871
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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” in Advances in Neural Information Processing Systems , 2018, pp. 4800–4810
2018
Cited alongside, same era.
2018
Cited alongside, same era.
H. Gao, Z. Wang, and S. Ji, “Large-scale learnable graph convolutional networks,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 1416–1424
2018
Cited alongside, same era.
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7794–7803
2018
Cited alongside, same era.
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka, “Representation learning on graphs with jumping knowledge networks,” in International Conference on Machine Learning , 2018, pp. 5453–5462
2018
Cited alongside, same era.
2018
Cited alongside, same era.
S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: a comprehensive review,” Computational Social Networks , vol. 6, no. 1, p. 11, 2019
2019
Cited alongside, same era.
J. Lee, I. Lee, and J. Kang, “Self-attention graph pooling,” in International Conference on Machine Learning , 2019, pp. 3734–3743
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
L. Cai and S. Ji, “A multi-scale approach for graph link prediction,” in Thirty-Fourth AAAI Conference on Artificial Intelligence , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Wang and S. Ji, “Second-order pooling for graph neural networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Later among the works it cites.
H. Yuan and S. Ji, “Structpool: Structured graph pooling via conditional random fields,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in International conference on machine learning , 2016, pp. 2014–2023
2023
Closest in time.
H. Gao and S. Ji, “Graph u-nets,” in International Conference on Machine Learning , 2019, pp. 2083–2092
2092
Closest in time.