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Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2012
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 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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Structural-rnn: Deep learning on spatio-temporal graphs
Ashesh Jain, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena · 2016
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Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Mgae: Marginalized graph autoencoder for graph clustering
Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang · 2017
Structured sequence modeling with graph convolutional recurrent networks
Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson · 2018
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Spatial temporal graph convolutional networks for skeleton-based action recognition
Sijie Yan, Yuanjun Xiong, and Dahua Lin · 2018
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Deep multi-view spatial-temporal network for taxi demand prediction
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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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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Adaptive graph convolutional neural networks
Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang · 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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Learning graph embedding with adversarial training methods
Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, and Chengqi Zhang · 2018
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Muhan Zhang and Yixin Chen · 2018
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung · 2018
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Geniepath: Graph neural networks with adaptive receptive paths
Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, and Yuan Qi · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu · 2019
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