[21] L. Han, B. Du, L. Sun, Y. Fu, Y. Lv, and H. Xiong (2021). "Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting". In: KDD (page 9).
[22] S. He, F. Bastani, S. Jagwani, E. Park, S. Abbar, M. Alizadeh, H. Balakrishnan, S. Chawla, S. Madden, and M. A. Sadeghi (2020). "Roadtagger: Robust road attribute inference with graph neural networks". In: AAAI (pages 3, 9).
[23] S. He, M. A. Sadeghi, S. Chawla, M. Alizadeh, H. Balakrishnan, and S. Madden (2021). "Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories". In: International Conference on Computer Vision (pages 1, 2, 10).
[24] H. Hong, Y. Lin, X. Yang, Z. Li, K. Fu, Z. Wang, X. Qie, and J. Ye (2020). "HetETA: Heterogeneous information network embedding for estimating time of arrival". In: KDD (page 9).
[25] W. Hu, M. Fey, H. Ren, M. Nakata, Y. Dong, and J. Leskovec (2021). "Ogb-lsc: A large-scale challenge for machine learning on graphs". In: NeurIPS Datasets and Benchmarks Track (pages 1, 8, 9).
[26] W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec (2020). "Open graph benchmark: Datasets for machine learning on graphs". In: NeurIPS (pages 1, 8, 9).
[27] B. Huang, B. Hooi, and K. Shu (2023). "TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks". In: arXiv preprint arXiv:2304.08640 (page 9).
[28] X. Huang, Y. Yang, Y. Wang, C. Wang, Z. Zhang, J. Xu, L. Chen, and M. Vazirgiannis (2022). "Dgraph: A large-scale financial dataset for graph anomaly detection". In: NeurIPS Datasets and Benchmarks Track (page 9).
[29] C. C. Ihueze and U. O. Onwurah (2018). "Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria". In: Accident analysis & prevention (pages 2, 10).
[30] H. V. Jagadish, J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, and C. Shahabi (2014). "Big data and its technical challenges". In: Communications of the ACM (page 3).
[31] W. Jiang and J. Luo (2022). "Graph neural network for traffic forecasting: A survey". In: Expert Systems with Applications (pages 2, 9).
[32] H. Ju, D. Li, A. Sharma, and H. R. Zhang (2023). "Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion". In: International Conference on Artificial Intelligence and Statistics. PMLR, pp. 6314-6341 (pages 2, 24).
[33] H. Ju, D. Li, and H. R. Zhang (2022). "Robust fine-tuning of deep neural networks with hessian-based generalization guarantees". In: International Conference on Machine Learning. PMLR, pp. 10431-10461 (page 24).
[34] T. N. Kipf and M. Welling (2016). "Semi-Supervised Classification with Graph Convolutional Networks". In: ICLR (pages 1, 6).
[35] D. Li, H. Ju, A. Sharma, and H. R. Zhang (2023a). "Boosting Multitask Learning on Graphs through Higher-Order Task Affinities". In: KDD (pages 2, 5, 24).
[36] D. Li, H. L. Nguyen, and H. R. Zhang (2023b). "Identification of Negative Transfers in Multitask Learning Using Surrogate Models". In: Transactions on Machine Learning Research (pages 2, 24).
[37] D. Li and H. Zhang (2021). "Improved regularization and robustness for fine-tuning in neural networks". In: Advances in Neural Information Processing Systems 34, pp. 27249-27262 (page 24).
[38] Y. Li, K. Fu, Z. Wang, C. Shahabi, J. Ye, and Y. Liu (2018a). "Multi-task representation learning for travel time estimation". In: KDD (pages 3, 9, 10).
[39] Y. Li, R. Yu, C. Shahabi, and Y. Liu (2018b). "Diffusion convolutional recurrent neural network: Data-driven traffic forecasting". In: ICLR (pages 1-3, 6, 10, 23).
[40] Z. Li, C. Huang, L. Xia, Y. Xu, and J. Pei (2022). "Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction". In: ICDE (page 9).
[41] L. Lin, Q. Wang, and A. W. Sadek (2015). "A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction". In: Transportation Research Part C: Emerging Technologies (pages 2, 8).