图卷积神经网络（持续更新）

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通用参考の交通流预测专题

• Graph convolutional networks: a comprehensive review：https://computationalsocialnetworks.springeropen.com/articles/10.1186/s40649-019-0069-y
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation 通用参考の交通流预测：https://link.springer.com/article/10.1007/s41019-020-00151-z
• A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges 通用参考の交通流预测：https://ieeexplore.ieee.org/document/9112608
• A Summary of Traffic Flow Forecasting Methods 通用参考の交通流预测：http://www.gljtkj.com/EN/Y2004/V21/I3/82
• A comprehensive survey on graph neural networks 通用参考の交通流预测：https://ieeexplore.ieee.org/abstract/document/9046288?casa_token=_-IU9Ixzx8kAAAAA:vcOheOMCzaaZRi5lykrhdY0CwfuoOiRU3lrdmA8uSXv1Auu8z9LrB67_JfrnSyjhoNEHbCAauz9atg
• 切比雪夫多项式（Chebyshev polynomials） 通用参考の交通流预测：https://proceedings.neurips.cc/paper_files/paper/2016/hash/04df4d434d481c5bb723be1b6df1ee65-Abstract.html
• 2023年全球道路安全状况报告（Global status report on road safety 2023） 通用参考の交通流预测：https://www.who.int/teams/social-determinants-of-health/safety-and-mobility/global-status-report-on-road-safety-2023

图嵌入

• Graph embedding techniques, applications, and performance: A survey：https://www.sciencedirect.com/science/article/pii/S0950705118301540
• A comprehensive survey of graph embedding: Problems, techniques, and applications：https://ieeexplore.ieee.org/abstract/document/8294302/?casa_token=RPHDwCwRd_sAAAAA:Us_qNvVZ0rIkhicT8MUJI87qKpF5diSGURb5rBkEtEn_Sru7qd_N5j4SESctQvL8kAM-bJLvzxQAVE8

通用参考の异常检测专题

• Detecting Road Traffic Events by Coupling Multiple Timeseries With a Nonparametric Bayesian Method：https://ieeexplore.ieee.org/abstract/document/6763098?casa_token=wPKB1S938vcAAAAA:il9gnh6pKOssqEYkYuzKor8XoYvhwYM_veqgVUjyCMoOqMMfnYtrnfnh7x4UKjw9UgsJaglC6we2nQ
• Investigating the impact of weather conditions and time of day on traffic flow characteristics：https://journals.ametsoc.org/view/journals/wcas/14/3/WCAS-D-22-0012.1.xml
• Variational Disentangled Graph Auto-Encoders for Link Prediction：https://arxiv.org/abs/2306.11315
• Graph neural networks for anomaly detection in industrial internet of things：https://ieeexplore.ieee.org/abstract/document/9471816?casa_token=c93zsFxKTZQAAAAA:Ud0fjHwZxW4orRAXglbEJnLVnZKSZJnmhwH0qH7dCGOlVBwODXGyVaD9Frzo2yV3ZOuXsCPA8FAaoA
• Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics：https://link.springer.com/article/10.1007/s00779-020-01474-4
• Urban anomaly analytics: Description, detection, and prediction：https://ieeexplore.ieee.org/abstract/document/9080109/
• Graph convolutional adversarial networks for spatiotemporal anomaly detection：https://ieeexplore.ieee.org/abstract/document/9669110/
• Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets：https://www.mdpi.com/1424-8220/23/1/486
• Graph neural network-based anomaly detection in multivariate time series：https://ojs.aaai.org/index.php/AAAI/article/view/16523
• GMAT-DU: Traffic anomaly prediction with fine spatiotemporal granularity in sparse data：https://ieeexplore.ieee.org/abstract/document/10061355/
• Graph anomaly detection with graph neural networks: Current status and challenges：https://ieeexplore.ieee.org/abstract/document/9906987/
• Anomaly detection with generative adversarial networks for multivariate time series：https://arxiv.org/abs/1809.04758
• A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder：https://ieeexplore.ieee.org/abstract/document/8279425/

图卷积神经网络

• GCN：https://arxiv.org/abs/1609.02907
• GAT：https://arxiv.org/abs/1710.10903
• GraphSAGE：https://proceedings.neurips.cc/paper_files/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html
• GIN：https://arxiv.org/abs/1810.00826
• DeepGCN：https://arxiv.org/abs/1904.03751
• PMLP：https://arxiv.org/abs/2212.09034
• DeepGCN：http://openaccess.thecvf.com/content_ICCV_2019/html/Li_DeepGCNs_Can_GCNs_Go_As_Deep_As_CNNs_ICCV_2019_paper.html
• 图的对比（Neighbor contrastive learning on learnable graph augmentation）：https://ojs.aaai.org/index.php/AAAI/article/view/26168

🔥 【强力推荐】PyTorch 的图神经网络库：https://pyg.org/