{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T01:56:31Z","timestamp":1771552591502,"version":"3.50.1"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:p>Traffic flow forecasting is a critical spatio-temporal data mining task with wide-ranging applications in intelligent route planning and dynamic traffic management. Recent advancements in deep learning, particularly through Graph Neural Networks (GNNs), have significantly enhanced the accuracy of these forecasts by capturing complex spatio-temporal dynamics. However, the scalability of GNNs remains a challenge due to their exponential growth in model complexity with increasing nodes in the graph. Existing methods to address this issue, including sparsification, decomposition, and kernel-based approaches, either do not fully resolve the complexity issue or risk compromising predictive accuracy. This paper introduces GraphSparseNet (GSNet), a novel framework designed to improve both the scalability and accuracy of GNN-based traffic forecasting models. GraphSparseNet is comprised of two core modules: the Feature Extractor and the Relational Compressor. These modules operate with linear time and space complexity, thereby reducing the overall computational complexity of the model to a linear scale. Our extensive experiments on multiple real-world datasets demonstrate that GraphSparseNet not only significantly reduces training time by 3.51x compared to state-of-the-art linear models but also maintains high predictive performance.<\/jats:p>","DOI":"10.14778\/3734839.3734862","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:01:06Z","timestamp":1756483266000},"page":"2295-2307","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["GraphSparseNet: A Novel Method for Large Scale Traffic Flow Prediction"],"prefix":"10.14778","volume":"18","author":[{"given":"Weiyang","family":"Kong","sequence":"first","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiqi","family":"Wu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubao","family":"Liu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","volume":"33","author":"Bai Lei","year":"2017","unstructured":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2017. Adaptive graph convolutional recurrent network for traffic forecasting. In Proceedings of the 34th International Conference on Neural Information Processing Systems, Vol. 33. 17804\u201317815."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274896"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3183903"},{"key":"e_1_2_1_4_1","volume-title":"Advances in neural information processing systems","author":"Drucker Harris","unstructured":"Harris Drucker, Christopher J Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik. 1996. Support vector regression machines. In Advances in neural information processing systems, Vol. 9."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599418"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Zheng Fang Qingqing Long Guojie Song and Kunqing Xie. 2021. Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting. (2021) 364\u2013373.","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641217"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467275"},{"key":"e_1_2_1_11_1","volume-title":"SOUP: A Fleet Management System for Passenger Demand Prediction and Competitive Taxi Supply. In 2021 IEEE 37th International Conference on Data Engineering. 2657\u20132660","author":"Hu Qi","year":"2021","unstructured":"Qi Hu, Lingfeng Ming, Ruijie Xi, Lu Chen, Christian S. Jensen, and Bolong Zheng. 2021. SOUP: A Fleet Management System for Passenger Demand Prediction and Competitive Taxi Supply. In 2021 IEEE 37th International Conference on Data Engineering. 2657\u20132660."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/326"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"e_1_2_1_14_1","volume-title":"Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey. arXiv preprint arXiv:2303.14483","author":"Jin Guangyin","year":"2023","unstructured":"Guangyin Jin, Yuxuan Liang, Yuchen Fang, Jincai Huang, Junbo Zhang, and Yu Zheng. 2023. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey. arXiv preprint arXiv:2303.14483 (2023)."},{"key":"e_1_2_1_15_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28707"},{"key":"e_1_2_1_17_1","volume-title":"GraphSparseNet: a Novel Method for Large Scale ***Trafffic Flow Prediction. arXiv preprint arXiv:2502.19823","author":"Kong Weiyang","year":"2025","unstructured":"Weiyang Kong, Kaiqi Wu, Sen Zhang, and Yubao Liu. 2025. GraphSparseNet: a Novel Method for Large Scale ***Trafffic Flow Prediction. arXiv preprint arXiv:2502.19823 (2025)."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"e_1_2_1_19_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820837"},{"key":"e_1_2_1_21_1","volume-title":"Advances in neural information processing systems","author":"Li Zhonghang","unstructured":"Zhonghang Li, Lianghao Xia, Yong Xu, and Chao Huang. 2023. GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. In Advances in neural information processing systems, Vol. 36. 70229\u201370246."},{"key":"e_1_2_1_22_1","volume-title":"ACFM: A Dynamic Spatial-Temporal Network for Traffic Prediction. arXiv preprint arXiv:1909.02902","author":"Liu Lingbo","year":"2019","unstructured":"Lingbo Liu, Jiajie Zhen, Guanbin Li, Geng Zhan, and Liang Lin. 2019. ACFM: A Dynamic Spatial-Temporal Network for Traffic Prediction. arXiv preprint arXiv:1909.02902 (2019)."},{"key":"e_1_2_1_23_1","first-page":"75354","article-title":"LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting","volume":"36","author":"Liu Xu","year":"2023","unstructured":"Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann. 2023. LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. In Advances in Neural Information Processing Systems, Vol. 36. 75354\u201375371.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_24_1","first-page":"2755","article-title":"Fine-Grained Urban Flow Inference","volume":"34","author":"Ouyang Kun","year":"2022","unstructured":"Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, and David S. Rosenblum. 2022. Fine-Grained Urban Flow Inference. IEEE Transactions on Knowledge and Data Engineering 34, 6 (2022), 2755\u20132770.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2932096"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2995855"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.473"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3038259"},{"key":"e_1_2_1_29_1","volume-title":"Advances in neural information processing systems","author":"Shi Xingjian","unstructured":"Xingjian Shi, Zhourong Chen, Hao Wang, DitYan Yeung, Waikin Wong, and Wangchun Woo. 2015. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In Advances in neural information processing systems, Vol. 28. 802\u2013810."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3008774"},{"key":"e_1_2_1_32_1","first-page":"1544","article-title":"A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges","volume":"34","author":"Tedjopurnomo David Alexander","year":"2022","unstructured":"David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, and A. K. Qin. 2022. A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. IEEE Transactions on Knowledge and Data Engineering 34, 4 (2022), 1544\u20131561.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"e_1_2_1_34_1","volume-title":"Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022","author":"Wu Yuankai","year":"2016","unstructured":"Yuankai Wu and Huachun Tan. 2016. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv preprint arXiv:1612.01022 (2016)."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"e_1_2_1_37_1","volume-title":"Modeling spatial-temporal dynamics for traffic prediction. arXiv preprint arXiv:1803.01254","author":"Yao Huaxiu","year":"2018","unstructured":"Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Yanwei Yu, and Zhenhui Li. 2018. Modeling spatial-temporal dynamics for traffic prediction. arXiv preprint arXiv:1803.01254 (2018)."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330887"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671662"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012245"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/519"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3734839.3734862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:01:10Z","timestamp":1756483270000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3734839.3734862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":44,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.14778\/3734839.3734862"],"URL":"https:\/\/doi.org\/10.14778\/3734839.3734862","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"2025-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}