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Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.
Graph wavenet for deep spatial-temporal graph modeling
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. 2019 · 1913
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Gaussian Processes for Machine Learning
Williams, C. K.; and Rasmussen, C. E. 2006 · 2006
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Kernelized probabilistic matrix factorization: Exploiting graphs and side information
Zhou, T.; Shan, H.; Banerjee, A.; and Sapiro, G. 2012 · 2012
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Fast multivariate spatio-temporal analysis via low rank tensor learning
Bahadori, M. T.; Yu, Q. R.; and Liu, Y. 2014 · 2014
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Spectral networks and locally connected networks on graphs
Bruna, J.; Zaremba, W.; Szlam, A.; and Lecun, Y. 2014 · 2014
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Statistics for Spatio-temporal Data
Cressie, N.; and Wikle, C. K. 2015 · 2015
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Collaborative filtering with graph information: Consistency and scalable methods
Rao, N.; Yu, H.-F.; Ravikumar, P. K.; and Dhillon, I. S. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
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Latent space model for road networks to predict time-varying traffic
Deng, D.; Shahabi, C.; Demiryurek, U.; Zhu, L.; Yu, R.; and Liu, Y. 2016 · 2016
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Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y.; Yu, R.; Shahabi, C.; and Liu, Y. 2017 · 2017
Cited alongside, same era.
Autoregressive tensor factorization for spatio-temporal predictions
Takeuchi, K.; Kashima, H.; and Ueda, N. 2017 · 2017
Cited alongside, same era.
Deep state space models for time series forecasting
Rangapuram, S. S.; Seeger, M. W.; Gasthaus, J.; Stella, L.; Wang, Y.; and Januschowski, T. 2018 · 2018
Cited alongside, same era.
Structured sequence modeling with graph convolutional recurrent networks
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
Zhang, J.; Shi, X.; Xie, J.; Ma, H.; King, I.; and Yeung, D. 2018 · 2018
Later among the works it cites.
Bounded Asymmetry in Road Networks
Mori, J. C. M.; and Samaranayake, S. 2019 · 2019
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High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
Salinas, D.; Bohlke-Schneider, M.; Callot, L.; Medico, R.; and Gasthaus, J. 2019 · 2019
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Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Sen, R.; Yu, H.-F.; and Dhillon, I. S. 2019 · 2019
Later among the works it cites.
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
Zhang, J.; Shi, X.; Zhao, S.; and King, I. 2019 · 2019
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Kriging Convolutional Networks
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Seo, Y.; Defferrard, M.; Vandergheynst, P.; and Bresson, X. 2018 · 2018
Cited alongside, same era.
Graph attention networks
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2018 · 2018
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2018 · 2018
Cited alongside, same era.
Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Yu, B.; Yin, H.; and Zhu, Z. 2018 · 2018
Cited alongside, same era.
Appleby, G.; Liu, L.; and Liu, L. 2020 · 2020
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GraphSAINT: Graph Sampling Based Inductive Learning Method
Zeng, H.; Zhou, H.; Srivastava, A.; Kannan, R.; and Prasanna, V. 2020 · 2020
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Inductive Matrix Completion Based on Graph Neural Networks
Zhang, M.; and Chen, Y. 2020 · 2020
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