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Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.
Vector autoregression and causality
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Utilizing real-world transportation data for accurate traffic prediction
Pan, B., Demiryurek, U., and Shahabi, C · 2012
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
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Urban computing: concepts, methodologies, and applications
Zheng, Y., Capra, L., Wolfson, O., and Yang, H · 2014
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
Cited alongside, same era.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Yu, B., Yin, H., and Zhu, Z · 2018
Cited alongside, same era.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H · 2019
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Connecting the dots: Multivariate time series forecasting with graph neural networks
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., and Zhang, C · 2020
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Graphsaint: Graph sampling based inductive learning method
Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V · 2020
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On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
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Spatial-temporal graph ode networks for traffic flow forecasting
Fang, Z., Long, Q., Song, G., and Xie, K · 2021
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Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., and Xiong, H · 2021
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Spatial-temporal fusion graph neural networks for traffic flow forecasting
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Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., and Zhang, J · 2019
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Graph wavenet for deep spatial-temporal graph modeling
Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C · 2019
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Deep learning based recommender system: A survey and new perspectives
Zhang, S., Yao, L., Sun, A., and Tay, Y · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T., Zhang, C., Krishnamurthy, A., Langford, J., and Agarwal, A · 2020
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Adaptive graph convolutional recurrent network for traffic forecasting
Bai, L., Yao, L., Li, C., Wang, X., and Wang, C · 2020
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Spectral temporal graph neural network for multivariate time-series forecasting
Cao, D., Wang, Y., Duan, J., Zhang, C., Zhu, X., Huang, C., Tong, Y., Xu, B., Bai, J., Tong, J., et al · 2020
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Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting
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Li, M. and Zhu, Z · 2021
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Tamp-s2gcnets: coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting
Chen, Y., Segovia-Dominguez, I., Coskunuzer, B., and Gel, Y · 2022
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Graph neural controlled differential equations for traffic forecasting
Choi, J., Choi, H., Hwang, J., and Park, N · 2022
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Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting
Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., and Li, P · 2022
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Nosmog: Learning noise-robust and structure-aware mlps on graphs
Tian, Y., Zhang, C., Guo, Z., Zhang, X., and Chawla, N. V · 2022
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Zhang, S., Liu, Y., Sun, Y., and Shah, N · 2022
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