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Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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dyngraph2vec: Capturing network dynamics using dynamic graph representation learning
Palash Goyal, Sujit Rokka Chhetri, and Arquimedes Canedo · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 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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Variational graph recurrent neural networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian · 2019
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Predicting dynamic embedding trajectory in temporal interaction networks
Srijan Kumar, Xikun Zhang, and Jure Leskovec · 2019
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Dyrep: Learning representations over dynamic graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
Earlier work this paper cites.
On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2020
Earlier work this paper cites.
Representation learning for dynamic graphs: A survey
Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart · 2020
Cited alongside, same era.
Evolvegcn: Evolving graph convolutional networks for dynamic graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson · 2020
Cited alongside, same era.
Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein · 2020
Cited alongside, same era.
Dysat: Deep neural representation learning on dynamic graphs via self-attention networks
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang · 2020
Cited alongside, same era.
Inductive representation learning in temporal networks via causal anonymous walks
Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li · 2020
Cited alongside, same era.
Continuous-time sequential recommendation with temporal graph collaborative transformer
Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, and Philip S Yu · 2021
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Self-supervised representation learning on dynamic graphs
Sheng Tian, Ruofan Wu, Leilei Shi, Liang Zhu, and Tao Xiong · 2021
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Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
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Apan: Asynchronous propagation attention network for real-time temporal graph embedding
Xuhong Wang, Ding Lyu, Mengjian Li, Yang Xia, Qi Yang, Xinwen Wang, Xinguang Wang, Ping Cui, Yupu Yang, Bowen Sun, et al · 2021
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Long short-term preference modeling for continuous-time sequential recommendation
Huixuan Chi, Hao Xu, Hao Fu, Mengya Liu, Mengdi Zhang, Yuji Yang, Qinfen Hao, and Wei Wu · 2022
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Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2020
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
Cited alongside, same era.
Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues
Khac-Hoai Nam Bui, Jiho Cho, and Hongsuk Yi · 2021
Cited alongside, same era.
On provable benefits of depth in training graph convolutional networks
Weilin Cong, Morteza Ramezani, and Mehrdad Mahdavi
Cited in the paper.
Dyformer: A scalable dynamic graph transformer with provable benefits on generalization ability
Weilin Cong, Yanhong Wu, Yuandong Tian, Mengting Gu, Yinglong Xia, Chun cheng Jason Chen, and Mehrdad Mahdavi
Cited in the paper.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein
Cited in the paper.
Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu
Cited in the paper.
Later among the works it cites.
Neighborhood-aware scalable temporal network representation learning
Yuhong Luo and Pan Li · 2022
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Provably expressive temporal graph networks
Amauri H Souza, Diego Mesquita, Samuel Kaski, and Vikas Garg · 2022
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Tgl: A general framework for temporal gnn training on billion-scale graphs
Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, and George Karypis · 2022
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