{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:23:22Z","timestamp":1787689402378,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780161","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"1626-1637","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["CSSG: A Continuous Spatio-temporal Graph Learning Framework with Scalable Spatial Granularity"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6550-5160","authenticated-orcid":false,"given":"Kaiwen","family":"Xia","sequence":"first","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3511-5559","authenticated-orcid":false,"given":"Li","family":"Lin","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3695-1161","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8366-0453","authenticated-orcid":false,"given":"Xinrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3609-2205","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-4224","authenticated-orcid":false,"given":"Xuming","family":"Hu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"international conference on machine learning. PMLR, 21-29","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning. PMLR, 21-29."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02776078"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28991"},{"key":"e_1_3_2_2_4_1","volume-title":"Principles of transportation engineering. PHI Learning Pvt","author":"Chakroborty Partha","unstructured":"Partha Chakroborty and Animesh Das. 2017. Principles of transportation engineering. PHI Learning Pvt. Ltd."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27802"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2024.104604"},{"key":"e_1_3_2_2_7_1","volume-title":"A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units. arXiv preprint arXiv:2403.07022","author":"Chen Liyue","year":"2024","unstructured":"Liyue Chen, Jiangyi Fang, Tengfei Liu, Shaosheng Cao, and Leye Wang. 2024c. A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units. arXiv preprint arXiv:2403.07022 (2024)."},{"key":"e_1_3_2_2_8_1","first-page":"8292","volume-title":"Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-Time Dynamics. In Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Chen Lanlan","year":"2024","unstructured":"Lanlan Chen, Kai Wu, Jian Lou, and Jing Liu. 2024 e. Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-Time Dynamics. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 8292-8301."},{"key":"e_1_3_2_2_9_1","volume-title":"Neural ordinary differential equations. Advances in neural information processing systems","author":"Chen Ricky TQ","year":"2018","unstructured":"Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural ordinary differential equations. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_10_1","volume-title":"Denoising High-Order Graph Clustering. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 3111-3124","author":"Chen Yonghao","year":"2024","unstructured":"Yonghao Chen, Ruibing Chen, Qiaoyun Li, Xiaozhao Fang, Jiaxing Li, and Wai Keung Wong. 2024a. Denoising High-Order Graph Clustering. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 3111-3124."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20587"},{"key":"e_1_3_2_2_12_1","volume-title":"Query and Fine-tune Framework. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 1340-1352","author":"Cui Yue","year":"2023","unstructured":"Yue Cui, Shuhao Li, Wenjin Deng, Zhaokun Zhang, Jing Zhao, Kai Zheng, and Xiaofang Zhou. 2023. ROI-demand Traffic Prediction: A Pre-train, Query and Fine-tune Framework. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 1340-1352."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25542"},{"key":"e_1_3_2_2_14_1","volume-title":"Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting. arXiv preprint arXiv:2405.10800","author":"Dong Zheng","year":"2024","unstructured":"Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, and Xuan Song. 2024. Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting. arXiv preprint arXiv:2405.10800 (2024)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16088"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599357"},{"key":"e_1_3_2_2_19_1","volume-title":"AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction. arXiv preprint arXiv:2402.03784","author":"Hettige Kethmi Hirushini","year":"2024","unstructured":"Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long, Gao Cong, and Jingyuan Wang. 2024. AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction. arXiv preprint arXiv:2402.03784 (2024)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3571729"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25555"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20322"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25976"},{"key":"e_1_3_2_2_25_1","volume-title":"SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting. 2024 IEEE 40th International Conference on Data Engineering (ICDE)","author":"Jiang Yue","year":"2024","unstructured":"Yue Jiang, Xiucheng Li, Yile Chen, Shuai Liu, Weilong Kong, Antonis F Lentzakis, and Gao Cong. 2024. SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting. 2024 IEEE 40th International Conference on Data Engineering (ICDE) (2024)."},{"key":"e_1_3_2_2_26_1","unstructured":"Ming Jin Qingsong Wen Yuxuan Liang Chaoli Zhang Siqiao Xue Xue Wang James Zhang Yi Wang Haifeng Chen Xiaoli Li et al. 2023. Large models for time series and spatio-temporal data: A survey and outlook. arXiv preprint arXiv:2310.10196 (2023)."},{"key":"e_1_3_2_2_27_1","volume-title":"Traffic flow dynamics: data, models and simulation. no. Book","author":"Kesting Arne","year":"2013","unstructured":"Arne Kesting and Martin Treiber. 2013. Traffic flow dynamics: data, models and simulation. no. Book, Whole)(Springer Berlin Heidelberg, Berlin, Heidelberg, 2013) (2013)."},{"key":"e_1_3_2_2_28_1","first-page":"6696","article-title":"Neural controlled differential equations for irregular time series","volume":"33","author":"Kidger Patrick","year":"2020","unstructured":"Patrick Kidger, James Morrill, James Foster, and Terry Lyons. 2020. Neural controlled differential equations for irregular time series. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6696-6707.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_29_1","volume-title":"Overview of traffic incident duration analysis and prediction. European transport research review","author":"Li Ruimin","year":"2018","unstructured":"Ruimin Li, Francisco C Pereira, and Moshe E Ben-Akiva. 2018. Overview of traffic incident duration analysis and prediction. European transport research review, Vol. 10, 2 (2018), 1-13."},{"key":"e_1_3_2_2_30_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Yaguang","year":"2017","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)."},{"key":"e_1_3_2_2_31_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Li Zhonghang","year":"2024","unstructured":"Zhonghang Li, Lianghao Xia, Yong Xu, and Chao Huang. 2024. GPT-ST: generative pre-training of spatio-temporal graph neural networks. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679854"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01200757"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-53303-4"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3002718"},{"key":"e_1_3_2_2_36_1","first-page":"1049","article-title":"Cubic spline interpolation","volume":"45","author":"McKinley Sky","year":"1998","unstructured":"Sky McKinley and Megan Levine. 1998. Cubic spline interpolation. College of the Redwoods, Vol. 45, 1 (1998), 1049-1060.","journal-title":"College of the Redwoods"},{"key":"e_1_3_2_2_37_1","volume-title":"A measurement theory for time geography. Geographical analysis","author":"Miller Harvey J","year":"2005","unstructured":"Harvey J Miller. 2005. A measurement theory for time geography. Geographical analysis, Vol. 37, 1 (2005), 17-45."},{"key":"e_1_3_2_2_38_1","volume-title":"Introduction to thermal systems engineering: thermodynamics, fluid mechanics, and heat transfer","author":"Moran Michael J","unstructured":"Michael J Moran, Howard N Shapiro, Bruce R Munson, and David P DeWitt. 2002. Introduction to thermal systems engineering: thermodynamics, fluid mechanics, and heat transfer. John Wiley & Sons."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3349397"},{"key":"e_1_3_2_2_40_1","volume-title":"Ricky TQ Chen, and David K Duvenaud","author":"Rubanova Yulia","year":"2019","unstructured":"Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud. 2019. Latent ordinary differential equations for irregularly-sampled time series. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614871"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671680"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599321"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00318"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_2_46_1","volume-title":"Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019a. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_2_47_1","volume-title":"Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019b. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599766"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709273"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737186"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00062"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330951"},{"key":"e_1_3_2_2_53_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu Bing","year":"2017","unstructured":"Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17761"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2872"}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780161","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:05:18Z","timestamp":1787688318000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":55,"alternative-id":["10.1145\/3770854.3780161","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780161","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}