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Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting.
Predicting taxi–passenger demand using streaming data
Luis Moreira-Matias, Joao Gama, Michel Ferreira, Joao Mendes-Moreira, and Luis Damas · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014
Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Transfer knowledge between cities
Ying Wei, Yu Zheng, and Qiang Yang · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Temporal convolutional networks for action segmentation and detection
Colin Lea, Michael D. Flynn, René Vidal, Austin Reiter, and Gregory D. Hager · 2017
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
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Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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Tdp: Personalized taxi demand prediction based on heterogeneous graph embedding
Zhenlong Zhu, Ruixuan Li, Minghui Shan, Yuhua Li, Lu Gao, Fei Wang, Jixing Xu, and Xiwu Gu · 2019
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Cross-city transfer learning for deep spatio-temporal prediction
Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, and Qiang Yang · 2019
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Learning from multiple cities: A meta-learning approach for spatial-temporal prediction
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li · 2019
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Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting
Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, and Quan Z. Sheng · 2019
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Graph-deep-learning-based inference of fine-grained air quality from mobile iot sensors
Tien Huu Do, Evaggelia Tsiligianni, Xuening Qin, Jelle Hofman, Valerio Panzica La Manna, Wilfried Philips, and Nikos Deligiannis · 2020
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Spatio-temporal meta learning for urban traffic prediction
Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, and Yu Zheng · 2020
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Graph few-shot learning via knowledge transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, and Zhenhui Li · 2020
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Graph prototypical networks for few-shot learning on attributed networks
Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu · 2020
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Didi chuxing gaia initiative
Didi-Chuxing · 2020
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Coupled layer-wise graph convolution for transportation demand prediction
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Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang · 2019
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Meta-transfer learning for few-shot learning
Qianru Sun, Yaoyao Liu, Tat-Seng Chua, and Bernt Schiele · 2019
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Meta-gnn: On few-shot node classification in graph meta-learning
Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng · 2019
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Graph wavelet neural network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng · 2019
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Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting
Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, and Haisong Zhang · 2020
Cited alongside, same era.
Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, and Hui Xiong · 2021
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Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction
Chunyang Wang, Yanmin Zhu, Tianzi Zang, Haobing Liu, and Jiadi Yu · 2021
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Relative and absolute location embedding for few-shot node classification on graph
Zemin Liu, Yuan Fang, Chenghao Liu, and Steven CH Hoi · 2021
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Adarnn: Adaptive learning and forecasting for time series
Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang · 2021
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