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We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs.
2015
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” Advances in neural information processing systems , vol. 29, pp. 3844–3852, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net, 2017. [Online]. Available: https://openreview.net/forum?id=SJU4ayYgl
2017
Earlier work this paper cites.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 1025–1035
2017
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 & Data Mining , 2018, pp. 974–983
2018
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, 2018. [Online]. Available: https://openreview.net/forum?id=rJXMpikCZ
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
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 . PMLR, 2018, pp. 5453–5462
2018
Earlier work this paper cites.
Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Thirty-Second AAAI conference on artificial intelligence , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in The World Wide Web Conference , 2019, pp. 417–426
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. Wang, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, S. Yang, and Y. Qi, “A semi-supervised graph attentive network for financial fraud detection,” in 2019 IEEE International Conference on Data Mining (ICDM) . IEEE, 2019, pp. 598–607
2019
Cited alongside, same era.
L. Yao, C. Mao, and Y. Luo, “Graph convolutional networks for text classification,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 7370–7377
2019
Cited alongside, same era.
2020
Later among the works it cites.
M. Liu, H. Gao, and S. Ji, “Towards deeper graph neural networks,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 338–348
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Hu, Y. Dong, K. Wang, and Y. Sun, “Heterogeneous graph transformer,” in Proceedings of The Web Conference 2020 , 2020, pp. 2704–2710
2020
Later among the works it cites.
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2019
Cited alongside, same era.
2019
Cited alongside, same era.
W. Shi and R. Rajkumar, “Point-gnn: Graph neural network for 3d object detection in a point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 1711–1719
2020
Cited alongside, same era.
Y. Zhang, H. Ren, and B. Khailany, “Grannite: Graph neural network inference for transferable power estimation,” in 2020 57th ACM/IEEE Design Automation Conference (DAC) . IEEE, 2020, pp. 1–6
2020
Cited alongside, same era.
Y.-C. Lu, S. S. K. Pentapati, L. Zhu, K. Samadi, and S. K. Lim, “Tp-gnn: a graph neural network framework for tier partitioning in monolithic 3d ics,” in 2020 57th ACM/IEEE Design Automation Conference (DAC) . IEEE, 2020, pp. 1–6
2020
Cited alongside, same era.
S. Vashishth, N. Yadati, and P. Talukdar, “Graph-based deep learning in natural language processing,” in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD , 2020, pp. 371–372
2020
Cited alongside, same era.
D. Chen, Y. Lin, W. Li, P. Li, J. Zhou, and X. Sun, “Measuring and relieving the over-smoothing problem for graph neural networks from the topological view,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 3438–3445
2020
Later among the works it cites.
2020
Later among the works it cites.
K. Wang, Z. Shen, C. Huang, C.-H. Wu, Y. Dong, and A. Kanakia, “Microsoft academic graph: When experts are not enough,” Quantitative Science Studies , vol. 1, no. 1, pp. 396–413, 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.
L. Wu, Y. Chen, H. Ji, and Y. Li, “Deep learning on graphs for natural language processing,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials , 2021, pp. 11–14
2021
Later among the works it cites.
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
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