{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:03:51Z","timestamp":1772121831896,"version":"3.50.1"},"reference-count":86,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62302397, 62433016"],"award-info":[{"award-number":["62302397, 62433016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Advanced Computing and Intelligence Engin","award":["2023-LYJJ-01-021"],"award-info":[{"award-number":["2023-LYJJ-01-021"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["D5000230191"],"award-info":[{"award-number":["D5000230191"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62302421"],"award-info":[{"award-number":["62302421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Basic and Applied Basic Research Fund in Guangdong Province","award":["2023A1515011280"],"award-info":[{"award-number":["2023A1515011280"]}]},{"name":"Ant Group through CCF-Ant Research Fund, Shenzhen Research Institute of Big Data","award":["SIF20240004"],"award-info":[{"award-number":["SIF20240004"]}]},{"name":"Hong Kong Jockey Club Charities Trust","award":["260920140"],"award-info":[{"award-number":["260920140"]}]},{"DOI":"10.13039\/501100003803","name":"University of Hong Kong","doi-asserted-by":"crossref","award":["2409100399"],"award-info":[{"award-number":["2409100399"]}],"id":[{"id":"10.13039\/501100003803","id-type":"DOI","asserted-by":"crossref"}]},{"name":"HKU Outstanding Research Student Supervisor","award":["Award 2022\u20132023"],"award-info":[{"award-number":["Award 2022\u20132023"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network where traffic data may not be available for every road. In this article, we study the\n            <jats:italic>stochastic weight completion<\/jats:italic>\n            (SWC) problem, which computes the weight distributions of missing road edges. This is difficult, due to the intricate temporal and spatial correlations among neighboring edges. Besides, the road network can be\n            <jats:italic>sparse<\/jats:italic>\n            , i.e., there is a lack of traveling information in a large portion of the network. To tackle these challenges, we propose a multi-granularity framework for\n            <jats:bold>Region-Wise Graph Completion (RegGC)<\/jats:bold>\n            . To learn coarse spatial correlations among distantly located roads, we construct a region-wise hypergraph neural architecture based on semantic region dependencies. For finer spatial correlations, we incorporate contextual road network properties (e.g., speed limits, lane counts, and road types). Moreover, it incorporates recent and periodic dimensions of road traffic. We evaluate RegGC against 10 existing methods on 3 real road network datasets. They show that RegGC is more effective and efficient than state-of-the-art solutions.\n          <\/jats:p>","DOI":"10.1145\/3719013","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:56:01Z","timestamp":1740149761000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Hypergraph-Enhanced Multi-Granularity Stochastic Weight Completion in Sparse Road Networks"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4347-1692","authenticated-orcid":false,"given":"Xiaolin","family":"Han","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, Xi\u2019an, China and Laboratory for Advanced Computing and Intelligence Engineering, Wuxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6919-194X","authenticated-orcid":false,"given":"Yikun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-8512","authenticated-orcid":false,"given":"Chenhao","family":"Ma","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong\u2014Shenzhen, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7249-8210","authenticated-orcid":false,"given":"Xuequn","family":"Shang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9480-9809","authenticated-orcid":false,"given":"Reynold","family":"Cheng","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0391-584X","authenticated-orcid":false,"given":"Tobias","family":"Grubenmann","sequence":"additional","affiliation":[{"name":"Edinburgh Napier University, Edinburgh, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5985-9886","authenticated-orcid":false,"given":"Xiaodong","family":"Li","sequence":"additional","affiliation":[{"name":"Xiamen University, Xiamen, China and Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"40","article-title":"Stochastic shortest path finding in path-centric uncertain road networks","author":"Andonov Georgi","year":"2018","unstructured":"Georgi Andonov and Bin Yang. 2018. Stochastic shortest path finding in path-centric uncertain road networks. In MDM, 40\u201345.","journal-title":"MDM"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.01.015"},{"issue":"3","key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1089\/cmb.2008.0078","article-title":"A model-based approach to gene clustering with missing observation reconstruction in a Markov random field framework","volume":"16","author":"Blanchet Juliette","year":"2009","unstructured":"Juliette Blanchet and Matthieu Vignes. 2009. A model-based approach to gene clustering with missing observation reconstruction in a Markov random field framework. Journal of Computational Biology 16, 3 (2009), 475\u2013486.","journal-title":"Journal of Computational Biology"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/267"},{"key":"e_1_3_2_6_2","first-page":"185","volume-title":"ACML","author":"Chen Weijun","year":"2023","unstructured":"Weijun Chen, Yanze Wang, Chengshuo Du, Zhenglong Jia, Feng Liu, and Ran Chen. 2023. Balanced spatial-temporal graph structure learning for multivariate time series forecasting: A trade-off between efficiency and flexibility. In ACML. PMLR, 185\u2013200."},{"key":"e_1_3_2_7_2","first-page":"257","volume-title":"SIGKDD","author":"Chiang Wei-Lin","year":"2019","unstructured":"Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In SIGKDD, 257\u2013266."},{"key":"e_1_3_2_8_2","volume-title":"Spectral Graph Theory","author":"Chung Fan R. K.","year":"1997","unstructured":"Fan R. K. Chung and Fan Chung Graham. 1997. Spectral Graph Theory. American Mathematical Society."},{"key":"e_1_3_2_9_2","first-page":"3074","volume-title":"SIGKDD","author":"Dai Rui","year":"2020","unstructured":"Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, and Kaikui Liu. 2020. Hybrid spatio-temporal graph convolutional network: Improving traffic prediction with navigation data. In SIGKDD, 3074\u20133082."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3564754"},{"key":"e_1_3_2_11_2","volume-title":"NIPS","volume":"29","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS, 29."},{"key":"e_1_3_2_12_2","first-page":"1525","volume-title":"SIGKDD","author":"Deng Dingxiong","year":"2016","unstructured":"Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, and Yans Liu. 2016. Latent space model for road networks to predict time-varying traffic. In SIGKDD, 1525\u20131534."},{"key":"e_1_3_2_13_2","first-page":"890","volume-title":"AAAI","volume":"110","author":"Diao Zulong","year":"2019","unstructured":"Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He. 2019. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. In AAAI, Article 110, 890\u2013897."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.09.015"},{"key":"e_1_3_2_15_2","first-page":"45","article-title":"Comparative study regarding the methods of interpolation","volume":"1","author":"Daniel Dumitru Paul","year":"2013","unstructured":"Paul Daniel Dumitru, Marin Plopeanu, and Dragos Badea. 2013. Comparative study regarding the methods of interpolation. Recent Advances in Geodesy and Geomatics Engineering 1 (2013), 45\u201352.","journal-title":"Recent Advances in Geodesy and Geomatics Engineering"},{"key":"e_1_3_2_16_2","first-page":"2069","volume-title":"IJCAI","author":"Feng Shanshan","year":"2015","unstructured":"Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. In IJCAI, 2069\u20132075."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3182052"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2018.00100"},{"key":"e_1_3_2_20_2","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume":"114","author":"Guo Shengnan","year":"2019","unstructured":"Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In AAAI, Article 114, 922\u2013929.","journal-title":"AAAI"},{"key":"e_1_3_2_21_2","first-page":"5415","volume-title":"IEEE Transactions on Knowledge and Data Engineering","volume":"11","author":"Guo Shengnan","year":"2021","unstructured":"Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Gao Cong. 2021. Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Transactions on Knowledge and Data Engineering (2021) 34, 11 (2021), 5415\u20135428."},{"key":"e_1_3_2_22_2","volume-title":"NIPS","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS, 30."},{"key":"e_1_3_2_23_2","first-page":"64","volume-title":"SDM. SIAM","author":"Han Xiaolin","year":"2022","unstructured":"Xiaolin Han, Reynold Cheng, Tobias Grubenmann, Silviu Maniu, Chenhao Ma, and Xiaodong Li. 2022. Leveraging contextual graphs for stochastic weight completion in sparse road networks. In SDM. SIAM, 64\u201372."},{"key":"e_1_3_2_24_2","first-page":"1493","volume-title":"Proceedings of the VLDB Endowment","volume":"15","author":"Han Xiaolin","year":"2022","unstructured":"Xiaolin Han, Reynold Cheng, Chenhao Ma, and Tobias Grubenmann. 2022. DeepTEA: Effective and efficient online time-dependent trajectory outlier detection. Proceedings of the VLDB Endowment 15, 7 (2022), 1493\u20131505."},{"key":"e_1_3_2_25_2","first-page":"1866","volume-title":"ICDE","author":"Han Xiaolin","year":"2020","unstructured":"Xiaolin Han, Tobias Grubenmann, Reynold Cheng, Sze Chun Wong, Xiaodong Li, and Wenya Sun. 2020. Traffic incident detection: A trajectory-based approach. In ICDE. IEEE, 1866\u20131869."},{"key":"e_1_3_2_26_2","first-page":"102418","article-title":"FDM: Effective and efficient incident detection on sparse trajectory data","author":"Han Xiaolin","year":"2024","unstructured":"Xiaolin Han, Tobias Grubenmann, Chenhao Ma, Xiaodong Li, Wenya Sun, Sze Chun Wong, Xuequn Shang, and Reynold Cheng. 2024. FDM: Effective and efficient incident detection on sparse trajectory data. Information Systems 125 (2024), 102418.","journal-title":"Information Systems"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.02.054"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00116"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119835"},{"key":"e_1_3_2_30_2","first-page":"1423","volume-title":"CIKM","author":"Huang Chao","year":"2018","unstructured":"Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V. Chawla. 2018. DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In CIKM, 1423\u20131432."},{"key":"e_1_3_2_31_2","first-page":"2355","article-title":"LSGCN: Long short-term traffic prediction with graph convolutional networks","volume":"7","author":"Huang Rongzhou","year":"2020","unstructured":"Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, and Weiyang Kong. 2020. LSGCN: Long short-term traffic prediction with graph convolutional networks. IJCAI 7 (2020), 2355\u20132361.","journal-title":"IJCAI"},{"issue":"1","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3412363","article-title":"Dynamic graph mining for multi-weight multi-destination route planning with deadlines constraints","volume":"15","author":"Huang Yu","year":"2020","unstructured":"Yu Huang, Josh Jia-Ching Ying, Philip S. Yu, and Vincent S. Tseng. 2020. Dynamic graph mining for multi-weight multi-destination route planning with deadlines constraints. ACM Transactions on Knowledge Discovery from Data 15, 1 (2020), 1\u201332.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.04.024"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/366"},{"key":"e_1_3_2_35_2","volume-title":"ICLR","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_36_2","unstructured":"Boris Knyazev Xiao Lin Mohamed R. Amer and Graham W. Taylor. 2018. Spectral multigraph networks for discovering and fusing relationships in molecules. arXiv:1811.09595. Retrieved from https:\/\/arxiv.org\/abs\/1811.09595"},{"key":"e_1_3_2_37_2","first-page":"140","volume-title":"ICDE","author":"Lee Jae-Gil","year":"2008","unstructured":"Jae-Gil Lee, Jiawei Han, and Xiaolei Li. 2008. Trajectory outlier detection: A partition-and-detect framework. In ICDE, 140\u2013149."},{"key":"e_1_3_2_38_2","first-page":"388","volume-title":"IEEE INFOCOM","author":"Lei Kai","year":"2019","unstructured":"Kai Lei, Meng Qin, Bo Bai, Gong Zhang, and Min Yang. 2019. GCN-GAN: A non-linear temporal link prediction model for weighted dynamic networks. In IEEE INFOCOM. IEEE, 388\u2013396."},{"key":"e_1_3_2_39_2","first-page":"835","volume-title":"ACM SIGKDD","author":"Lei Xiaoliang","year":"2022","unstructured":"Xiaoliang Lei, Hao Mei, Bin Shi, and Hua Wei. 2022. Modeling network-level traffic flow transitions on sparse data. In ACM SIGKDD, 835\u2013845."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3041234"},{"key":"e_1_3_2_41_2","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv:1707.01926. Retrieved from https:\/\/arxiv.org\/abs\/1707.01926"},{"key":"e_1_3_2_42_2","first-page":"338","volume-title":"ACM SIGKDD","author":"Liu Meng","year":"2020","unstructured":"Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020. Towards deeper graph neural networks. In ACM SIGKDD, 338\u2013348."},{"key":"e_1_3_2_43_2","first-page":"949","volume-title":"ICDE","author":"Liu Yiding","year":"2020","unstructured":"Yiding Liu, Kaiqi Zhao, Gao Cong, and Zhifeng Bao. 2020. Online anomalous trajectory detection with deep generative sequence modeling. In ICDE. IEEE, 949\u2013960."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551826"},{"key":"e_1_3_2_45_2","first-page":"845","volume-title":"SIGMOD","author":"Ma Chenhao","year":"2022","unstructured":"Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V. S. Lakshmanan, and Xiaolin Han. 2022. A convex-programming approach for efficient directed densest subgraph discovery. In SIGMOD, 845\u2013859."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-023-00805-0"},{"key":"e_1_3_2_47_2","first-page":"1051","volume-title":"ACM SIGMOD","author":"Ma Chenhao","year":"2020","unstructured":"Chenhao Ma, Yixiang Fang, Reynold Cheng, Laks V. S. Lakshmanan, Wenjie Zhang, and Xuemin Lin. 2020. Efficient algorithms for densest subgraph discovery on large directed graphs. In ACM SIGMOD, 1051\u20131066."},{"issue":"7","key":"e_1_3_2_48_2","first-page":"7164","article-title":"MBA-STNet: Bayes-enhanced discriminative multi-task learning for flow prediction","volume":"35","author":"Miao Hao","year":"2022","unstructured":"Hao Miao, Jiaxing Shen, Jiannong Cao, Jiangnan Xia, and Senzhang Wang. 2022. MBA-STNet: Bayes-enhanced discriminative multi-task learning for flow prediction. IEEE Transactions on Knowledge and Data Engineering 35, 7 (2022), 7164\u20137177.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_49_2","first-page":"1","volume-title":"ACM Transactions on Knowledge Discovery from Data","volume":"18","author":"Miller Benjamin A.","year":"2023","unstructured":"Benjamin A. Miller, Zohair Shafi, Wheeler Ruml, Yevgeniy Vorobeychik, Tina Eliassi-Rad, and Scott Alfeld. 2023. Attacking shortest paths by cutting edges. ACM Transactions on Knowledge Discovery from Data 18, 2 (2023), 1\u201342."},{"key":"e_1_3_2_50_2","volume-title":"NIPS","volume":"31","author":"Narasimhan Medhini","year":"2018","unstructured":"Medhini Narasimhan, Svetlana Lazebnik, and Alexander G. Schwing. 2018. Out of the box: Reasoning with graph convolution nets for factual visual question answering. In NIPS, 31."},{"key":"e_1_3_2_51_2","volume-title":"SIGSPATIAL","author":"Newson Paul","year":"2009","unstructured":"Paul Newson and John Krumm. 2009. Hidden Markov map matching through noise and sparseness. In SIGSPATIAL."},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s00778-019-00585-6","article-title":"Fast stochastic routing under time-varying uncertainty","volume":"29","author":"Pedersen Simon Aagaard","year":"2019","unstructured":"Simon Aagaard Pedersen, Bin Yang, and Christian S. Jensen. 2019. Fast stochastic routing under time-varying uncertainty. The VLDB Journal 29 (2019), 819\u2013839.","journal-title":"The VLDB Journal"},{"issue":"9","key":"e_1_3_2_53_2","first-page":"1555","article-title":"Anytime stochastic routing with hybrid learning","volume":"13","author":"Pedersen Simon Aagaard","year":"2020","unstructured":"Simon Aagaard Pedersen, Bin Yang, and Christian S. Jensen. 2020. Anytime stochastic routing with hybrid learning. The VLDB Journal 13, 9 (2020), 1555\u20131567.","journal-title":"The VLDB Journal"},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.trb.2017.12.015","article-title":"Pruning algorithm for the least expected travel time path on stochastic and time-dependent networks","author":"Arun Prakash A.","year":"2018","unstructured":"A. Arun Prakash. 2018. Pruning algorithm for the least expected travel time path on stochastic and time-dependent networks. Transportation Research Part B: Methodological 108 (2018), 127\u2013147.","journal-title":"Transportation Research Part B: Methodological"},{"key":"e_1_3_2_55_2","first-page":"1027","volume-title":"SIGKDD","author":"Shang Jingbo","year":"2014","unstructured":"Jingbo Shang, Yu Zheng, Wenzhu Tong, Eric Chang, and Yong Yu. 2014. Inferring gas consumption and pollution emission of vehicles throughout a city. In SIGKDD, 1027\u20131036."},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"issue":"20","key":"e_1_3_2_57_2","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In ICLR, 1050(20): 10\u201348550.","journal-title":"ICLR"},{"key":"e_1_3_2_58_2","first-page":"103","volume-title":"ACM SIGKDD","author":"Wang Haibo","year":"2012","unstructured":"Haibo Wang and Kuien Liu. 2012. User oriented trajectory similarity search. In ACM SIGKDD, 103\u2013110."},{"key":"e_1_3_2_59_2","first-page":"499","article-title":"Traffic speed prediction and congestion source exploration: A deep learning method","author":"Wang Jingyuan","year":"2016","unstructured":"Jingyuan Wang, Qian Gu, Junjie Wu, Guannan Liu, and Zhang Xiong. 2016. Traffic speed prediction and congestion source exploration: A deep learning method. In ICDM, 499\u2013508.","journal-title":"ICDM"},{"issue":"2024","key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"8770","DOI":"10.1109\/TITS.2024.3367779","article-title":"Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks","volume":"8","author":"Wang Qingyi","year":"2024","unstructured":"Qingyi Wang, Shenhao Wang, Dingyi Zhuang, Haris Koutsopoulos, and Jinhua Zhao. 2024. Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks. IEEE Transactions on Intelligent Transportation Systems25, 8 (2024), 8770\u20138781.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_61_2","first-page":"399","article-title":"Videos as space-time region graphs","author":"Wang Xiaolong","year":"2018","unstructured":"Xiaolong Wang and Abhinav Gupta. 2018. Videos as space-time region graphs. In ECCV, 399\u2013417.","journal-title":"ECCV"},{"key":"e_1_3_2_62_2","first-page":"2022","volume-title":"WWW","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous graph attention network. In WWW, 2022\u20132032."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"issue":"2","key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"bbad069","DOI":"10.1093\/bib\/bbad069","article-title":"Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information","volume":"24","author":"Wang Yongtian","year":"2023","unstructured":"Yongtian Wang, Xinmeng Liu, Yewei Shen, Xuerui Song, Tao Wang, Xuequn Shang, and Jiajie Peng. 2023. Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information. Briefings in Bioinformatics 24, 2 (2023), bbad069.","journal-title":"Briefings in Bioinformatics"},{"key":"e_1_3_2_65_2","first-page":"1","article-title":"Integrative graph-based framework for predicting circRNA drug resistance using disease contextualization and deep learning","author":"Wang Yongtian","year":"2024","unstructured":"Yongtian Wang, Wenkai Shen, Yewei Shen, Shang Feng, Tao Wang, Xuequn Shang, and Jiajie Peng. 2024. Integrative graph-based framework for predicting circRNA drug resistance using disease contextualization and deep learning. IEEE Journal of Biomedical and Health Informatics (2024), 1\u201312.","journal-title":"IEEE Journal of Biomedical and Health Informatics (2024)"},{"key":"e_1_3_2_66_2","first-page":"3571","volume-title":"IJCAI","author":"Wang Yang","year":"2018","unstructured":"Yang Wang, Yiwei Xiao, Xike Xie, Ruoyu Chen, and Hengchang Liu. 2018. Real-time traffic pattern analysis and inference with sparse video surveillance information. In IJCAI, 3571\u20133577."},{"key":"e_1_3_2_67_2","unstructured":"Zheng Wang Cheng Long Gao Cong and Yiding Liu. 2020. Efficient and effective similar subtrajectory search with deep reinforcement learning. arXiv:2003.02542. Retrieved from https:\/\/arxiv.org\/abs\/2003.02542"},{"key":"e_1_3_2_68_2","first-page":"1454","volume-title":"ITSC","author":"Wang Zepu","year":"2023","unstructured":"Zepu Wang, Dingyi Zhuang, Yankai Li, Jinhua Zhao, Peng Sun, Shenhao Wang, and Yulin Hu. 2023. ST-GIN: An uncertainty quantification approach in traffic data imputation with spatio-temporal graph attention and bidirectional recurrent united neural networks. In ITSC. IEEE, 1454\u20131459."},{"key":"e_1_3_2_69_2","doi-asserted-by":"crossref","unstructured":"Hua Wei Chacha Chen Chang Liu Guanjie Zheng and Zhenhui Li. [n. d.]. Learning to simulate on sparse trajectory data 530\u2013545.","DOI":"10.1007\/978-3-030-67667-4_32"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.10.011"},{"key":"e_1_3_2_71_2","first-page":"10432","volume-title":"ICML","author":"Xhonneux Louis-Pascal","year":"2020","unstructured":"Louis-Pascal Xhonneux, Meng Qu, and Jian Tang. 2020. Continuous graph neural networks. In ICML. PMLR, 10432\u201310441."},{"key":"e_1_3_2_72_2","first-page":"1631","volume-title":"IJCAI","author":"Xia Lianghao","year":"2021","unstructured":"Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, and Tianyi Chen. 2021. Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning. In IJCAI, 1631\u20131637."},{"key":"e_1_3_2_73_2","first-page":"1478","volume-title":"Proceedings of the VLDB Endowment","volume":"10","author":"Xie Dong","year":"2017","unstructured":"Dong Xie, Feifei Li, and Jeff M. Phillips. 2017. Distributed trajectory similarity search. Proceedings of the VLDB Endowment 10, 11 (2017), 1478\u20131489."},{"issue":"1","key":"e_1_3_2_74_2","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"32","author":"Yan Sijie","year":"2018","unstructured":"Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. Proceedings of the AAAI Conference on Artificial Intelligence 32, 1 (2018).","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s00778-017-0491-4","article-title":"PACE: A PAth-CEntric paradigm for stochastic path finding","volume":"27","author":"Yang Bin","year":"2018","unstructured":"Bin Yang, Jian Dai, Chenjuan Guo, Christian S. Jensen, and Jilin Hu. 2018. PACE: A PAth-CEntric paradigm for stochastic path finding. The VLDB Journal 27 (2018), 153\u2013178.","journal-title":"The VLDB Journal"},{"issue":"9","key":"e_1_3_2_76_2","first-page":"769","article-title":"Travel cost inference from sparse, spatio temporally correlated time series using Markov models","volume":"6","author":"Yang Bin","year":"2013","unstructured":"Bin Yang, Chenjuan Guo, and Christian S. Jensen. 2013. Travel cost inference from sparse, spatio temporally correlated time series using Markov models. The VLDB Journal 6, 9 (2013), 769\u2013780.","journal-title":"The VLDB Journal"},{"key":"e_1_3_2_77_2","first-page":"136","article-title":"Stochastic skyline route planning under time-varying uncertainty","author":"Yang Bin","year":"2014","unstructured":"Bin Yang, Chenjuan Guo, Christian S. Jensen, Manohar Kaul, and Shuo Shang. 2014. Stochastic skyline route planning under time-varying uncertainty. In ICDE,136-147.","journal-title":"ICDE"},{"key":"e_1_3_2_78_2","first-page":"1245","article-title":"Bridging collaborative filtering and semi-supervised learning: A neural approach for poi recommendation","author":"Yang Carl","year":"2017","unstructured":"Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, and Jiawei Han. 2017. Bridging collaborative filtering and semi-supervised learning: A neural approach for poi recommendation. In SIGKDD, 1245\u20131254.","journal-title":"SIGKDD"},{"key":"e_1_3_2_79_2","first-page":"2006","article-title":"Learning to rank paths in spatial networks","author":"Yang Sean Bin","year":"2020","unstructured":"Sean Bin Yang and Bin Yang. 2020. Learning to rank paths in spatial networks. In ICDE, 2006\u20132009.","journal-title":"ICDE"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"e_1_3_2_81_2","first-page":"3366","volume-title":"ACM SIGKDD","author":"Yi Jaehyuk","year":"2020","unstructured":"Jaehyuk Yi and Jinkyoo Park. 2020. Hypergraph convolutional recurrent neural network. In ACM SIGKDD, 3366\u20133376."},{"key":"e_1_3_2_82_2","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","volume":"31","author":"Zhang Junbo","year":"2017","unstructured":"Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In AAAI, Vol. 31.","journal-title":"AAAI"},{"key":"e_1_3_2_83_2","first-page":"1","volume-title":"SIGSPATIAL","author":"Zhang Minxing","year":"2022","unstructured":"Minxing Zhang, Dazhou Yu, Yun Li, and Liang Zhao. 2022. Deep geometric neural network for spatial interpolation. In SIGSPATIAL, 1\u20134."},{"key":"e_1_3_2_84_2","first-page":"842","volume-title":"ACM SIGKDD","author":"Zhang Yingxue","year":"2020","unstructured":"Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, and Jun Luo. 2020. Curb-GAN: Conditional urban traffic estimation through spatio-temporal generative adversarial networks. In ACM SIGKDD, 842\u2013852."},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3007194"},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.1609\/aaai.v35i5.16603","article-title":"Modeling heterogeneous relations across multiple modes for potential crowd flow prediction","volume":"35","author":"Zhou Qiang","year":"2021","unstructured":"Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, and Xiao Zhang. 2021. Modeling heterogeneous relations across multiple modes for potential crowd flow prediction. In AAAI, Vol. 35, 4723\u20134731.","journal-title":"AAAI"},{"key":"e_1_3_2_87_2","first-page":"1868","volume-title":"WWW","author":"Zhou Zhengyang","year":"2021","unstructured":"Zhengyang Zhou, Yang Wang, Xike Xie, Lei Qiao, and Yuantao Li. 2021. Stuanet: Understanding uncertainty in spatiotemporal collective human mobility. In WWW, 1868\u20131879."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3719013","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3719013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:08Z","timestamp":1750295948000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3719013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"references-count":86,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,4,30]]}},"alternative-id":["10.1145\/3719013"],"URL":"https:\/\/doi.org\/10.1145\/3719013","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]},"assertion":[{"value":"2024-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}