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This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.
2014
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K. Cho, B. van Merriënboer, 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 (EMNLP) , 2014, pp. 1724–1734
2014
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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 , vol. 30, 2017, pp. 5998–6008
2017
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T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the International Conference on Learning Representations , 2017
2017
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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
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J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proc. of ICML , 2017, pp. 1263–1272
2017
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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
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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
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Y. Seo, M. Defferrard, P. Vandergheynst, and X. Bresson, “Structured sequence modeling with graph convolutional recurrent networks,” in Advances in Neural Information Processing Systems . Springer, 2018, pp. 362–373
2018
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Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in Proceedings of the International Conference on Learning Representations , 2018
2018
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J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung, “Gaan: Gated attention networks for learning on large and spatiotemporal graphs,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence , 2018
2018
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S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
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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 I International Joint Conference on Artificial Intelligence , 2018, pp. 3634–3640
2018
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G. Tang, M. Müller, A. Rios, and R. Sennrich, “Why self-attention? a targeted evaluation of neural machine translation architectures,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics , 2018, pp. 4263–4272
2018
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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.
S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in Proceedings of AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 922–929
2019
Cited alongside, same era.
H. Gao and S. Ji, “Graph representation learning via hard and channel-wise attention networks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 741–749
2019
Cited alongside, same era.
J. Klicpera, S. Weißenberger, and S. Günnemann, “Diffusion improves graph learning,” in Advances in Neural Information Processing Systems , 2019, pp. 13 354–13 366
2019
Cited alongside, same era.
Q. Zhang, J. Chang, G. Meng, S. Xiang, and C. Pan, “Spatio-temporal graph structure learning for traffic forecasting,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 1177–1185
2020
Later among the works it cites.
C. Zheng, X. Fan, C. Wang, and J. Qi, “Gman: A graph multi-attention network for traffic prediction,” in Proceedings of AAAI Conference on Artificial Intelligence , 2020
2020
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C. Song, Y. Lin, S. Guo, and H. Wan, “Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,” in Proceedings of AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 914–921
2020
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B. Paassen, D. Grattarola, D. Zambon, C. Alippi, and B. E. Hammer, “Graph edit networks,” in International Conference on Learning Representations , 2020
2020
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Z. Pan, Y. Liang, W. Wang, Y. Yu, Y. Zheng, and J. Zhang, “Urban traffic prediction from spatio-temporal data using deep meta learning,” in KDD . ACM, 2019, pp. 1720–1730
2019
Cited alongside, same era.
B. Li, X. Li, Z. Zhang, and F. Wu, “Spatio-temporal graph routing for skeleton-based action recognition,” in Proceedings of AAAI Conference on Artificial Intelligence , 2019, pp. 8561–8568
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 Twenty-Eighth International Joint Conference on Artificial Intelligence , 2019
2019
Cited alongside, same era.
D. Zambon, D. Grattarola, L. Livi, and C. Alippi, “Autoregressive models for sequences of graphs,” in 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2019, pp. 1–8
2019
Cited alongside, same era.
2019
Cited alongside, same era.
C. Park, C. Lee, H. Bahng, K. Kim, S. Jin, S. Ko, J. Choo et al. , “Stgrat: a spatio-temporal graph attention network for traffic forecasting,” in Proceedings of the Conference on Information and Knowledge Management , 2020
2020
Cited alongside, same era.
X. Wang, Y. Ma, Y. Wang, W. Jin, X. Wang, J. Tang, C. Jia, and J. Yu, “Traffic flow prediction via spatial temporal graph neural network,” in Proceedings of the World Wide Web Conference , 2020, pp. 1082–1092
2020
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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
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F. M. Bianchi, D. Grattarola, L. Livi, and C. Alippi, “Graph neural networks with convolutional arma filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
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S. Wan, Y. Zhan, L. Liu, B. Yu, S. Pan, and C. Gong, “Contrastive graph poisson networks: Semi-supervised learning with extremely limited labels,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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L. Mengzhang and Z. Zhanxing, “Spatial-temporal fusion graph neural networks for traffic flow forecasting,” in Proceedings of AAAI Conference on Artificial Intelligence , 2021
2021
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2021
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E. Isufi and G. Mazzola, “Graph-time convolutional neural networks,” in 2021 IEEE Data Science and Learning Workshop (DSLW) . IEEE, 2021, pp. 1–6
2021
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Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and P. Yu, “Graph self-supervised learning: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
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2022
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M. Jin, Y. Zheng, Y.-F. Li, S. Chen, B. Yang, and S. Pan, “Multivariate time series forecasting with dynamic graph neural ODEs,” arXiv 2202.08408 , 2022
2022
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S. Hadou, C. I. Kanatsoulis, and A. Ribeiro, “Space-time graph neural networks,” in International Conference on Learning Representations , 2022
2022
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