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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n                    Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and temporal graph neural networks to deal with the complex dependence between the traffic in adjacent areas. However, existing graph neural network based approaches suffer from several critical drawbacks, including improper graph representation of urban traffic data, lack of semantic correlation modeling among graph nodes, and coarse-grained exploitation of external factors. To address these issues, we propose\n                    <jats:italic toggle=\"yes\">DiffUFP<\/jats:italic>\n                    , a novel probabilistic graph-based framework for UFP. DiffUFP\u00a0consists of two key designs: (1) a semantic region dynamic extraction method that effectively captures the underlying traffic network topology, and (2) a conditional denoising score-based adjacency matrix generator that takes spatial, temporal, and external factors into account when constructing the adjacency matrix rather than simply concatenation in existing studies. Extensive experiments conducted on real-world datasets demonstrate the superiority of DiffUFP\u00a0over the state-of-the-art UFP\u00a0models and the effect of the two specific modules.\n                  <\/jats:p>","DOI":"10.1145\/3655629","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T07:10:40Z","timestamp":1711955440000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Score-based Graph Learning for Urban Flow Prediction"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8822-6957","authenticated-orcid":false,"given":"Pengyu","family":"Wang","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-9242","authenticated-orcid":false,"given":"Xucheng","family":"Luo","sequence":"additional","affiliation":[{"name":"UESTC","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7364-8324","authenticated-orcid":false,"given":"Wenxin","family":"Tai","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1474-3169","authenticated-orcid":false,"given":"Kunpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Maryland, College Park","place":["College Park, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8839-6278","authenticated-orcid":false,"given":"Goce","family":"Trajcevsky","sequence":"additional","affiliation":[{"name":"Iowa State University","place":["Ames, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8038-8150","authenticated-orcid":false,"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/3000850.3000887"},{"key":"e_1_3_3_3_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Brock Andrew","year":"2019","unstructured":"Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. 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