近三年用于时空数据挖掘的GNN论文汇总

综述

1. Graph Neural Network for Traffic Forecasting: A Survey. arXiv 2021.

顶级会议论文

—2021—

1. Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network. AAAI 2021. code (https://github.com/jillbetty001/ST-GDN)
2. Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting. AAAI 2021. code (https://github.com/MengzhangLI/STFGNN)
3. Hierarchical Graph Convolution Networks for Traffic Forecasting. AAAI 2021. code (https://github.com/guokan987/HGCN)
4. FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting. AAAI 2021. code (https://github.com/boreshkinai/fc-gaga)
5. Coupled Layer-wise Graph Convolution for Transportation Demand Prediction. AAAI 2021. code (https://github.com/Essaim/CGCDemandPrediction)
6. Discrete Graph Structure Learning for Forecasting Multiple Time Series. ICLR 2021. code (https://github.com/chaoshangcs/GTS)

—2020—
7. Adaptive graph convolutional recurrent network for traffic forecasting. NeurIPS 2020.
8. PM2. 5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2. 5 Forecasting. SIGSPATIAL 2020. code (https://github.com/shawnwang-tech/PM2.5-GNN)
9. Spatial-temporal sychronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. AAAI 2020. code (https://github.com/wanhuaiyu/STSGCN)
10. GMAN: A Graph Multi-Attention Network for Traffic Prediction. AAAI 2020. code (https://github.com/zhengchuanpan/GMAN)
11. Spatio-Temporal Graph Structure Learning for Traffic Forecasting. AAAI 2020.
12. STGRAT: A Spatio-temporal Graph Attention Network for Traffic Forecasting. AAAI 2020.
13. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network. ICDE 2020.
14. Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks. ICDE 2020. code (https://github.com/hujilin1229/od-pred)
15. Learning Effective Road Network Representation with Hierarchical Graph Neural Networks. KDD 2020.
16. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. KDD 2020. code (https://github.com/nnzhan/MTGNN)
17. Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. KDD 2020.
18. ConSTGAT: Contextual Spatial-Temporal Graph Attention Network for Travel Time Estimation at Baidu Maps. KDD 2020.
19. Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data. KDD 2020.
20. Dynamic Heterogeneous Graph Neural Network for Real-time Event Prediction. KDD 2020.

—2019—
21. Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting. AAAI 2019.
22. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. AAAI 2019. code(https://github.com/guoshnBJTU/ASTGCN-r-pytorch)
23. Graph wavenet for deep spatial-temporal graph modeling. IJCAI 2019. (https://github.com/nnzhan/Graph-WaveNet)

—2018—
24. Diffusion Convolutional Recurrent Neural Network: Data-driven Traffic Forecasting. ICLR 2018.
25. Bike flow prediction with multi-graph convolutional networks. SIGSPATIAL 2018. (https://github.com/Di-Chai/GraphCNN-Bike)
26. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI 2018. code (https://github.com/liyaguang/DCRNN)
27. Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction. CIKM 2018.

期刊论文

—2021—

1. Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting. TKDE 2021.
2. A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction. TITS 2021.
3. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data. TITS 2021.
4. GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs. IEEE Signal Processing Letters 2021.
5. Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transportation Research Part C 2021. code (https://github.com/kejintao/ST-ED-RMGC)
6. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transportation Research Part C: Emerging Technologies 2021.
7. TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network. Information Sciences 2021.
8. Long-term Origin-Destination Demand Prediction with Graph Deep Learning. IEEE Transactions on Big Data 2021.

—2020—
9. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting. Information Sciences 2020. code (https://github.com/RingBDStack/GCNN-In-Traffic)
10. Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks. TKDE 2020.

预印本论文

—2021—

1. HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method. arXiv 2021.
2. Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction. arXiv 2021.
3. Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph. arXiv 2021.
4. Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network. arXiv 2021.
5. Bayesian Graph Convolutional Network for Traffic Prediction. arXiv 2021.

—2020—
6. Physical-Virtual Collaboration Graph Network for Station-Level Metro Ridership Prediction. arXiv 2020. code (https://github.com/ivechan/PVCGN)
7. Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting. arXiv 2020. code (https://github.com/tanwimallick/TL-DCRNN)

—2019—
8. Incrementally Improving Graph WaveNet Performance on Traffic Prediction. arXiv 2019. code (https://github.com/sshleifer/Graph-WaveNet)
9. STGRAT: A Spatio-temporal Graph Attention Network for Traffic Forecasting. arXiv 2019.

—2018—
10. Efficient Metropolitan Traffic Prediction Based on Graph Recurrent Neural Network. arXiv 2018.
11. Dynamic spatio-temporal graph-based cnns for traffic prediction. arXiv 2018.

小结

欢迎各位在评论区留言，补充，提出您的宝贵意见和建议，我们一起学习，成长，进步。

参考资料

1. https://github.com/xiepeng21/research_spatio-temporal-data-mining
2. https://github.com/jwwthu/GNN4Traffic/
3. https://github.com/Knowledge-Precipitation-Tribe/Spatio-Temporal-papers
4. https://github.com/datawhalechina/spatio-temporal-papers