{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T03:25:24Z","timestamp":1770348324452,"version":"3.49.0"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272087"],"award-info":[{"award-number":["62272087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFFK0116"],"award-info":[{"award-number":["2024YFFK0116"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1016\/j.inffus.2025.103464","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T07:38:46Z","timestamp":1752651526000},"page":"103464","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Multi-Scale Temporal Graph Contrastive Embedding for urban region representation"],"prefix":"10.1016","volume":"125","author":[{"given":"Yue","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4075-5355","authenticated-orcid":false,"given":"Xinzheng","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Jiahui","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Min","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.inffus.2025.103464_b1","series-title":"World Urbanization Prospects","author":"of Economic","year":"2018"},{"key":"10.1016\/j.inffus.2025.103464_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102606","article-title":"Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook","volume":"113","author":"Zou","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2025.103464_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110087","article-title":"Urban hotspot forecasting via automated spatio-temporal information fusion","volume":"136","author":"Jin","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.inffus.2025.103464_b4","article-title":"Disentangling the hourly dynamics of mixed urban function: A multimodal fusion perspective using dynamic graphs","author":"Cao","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2025.103464_b5","doi-asserted-by":"crossref","unstructured":"Y. Fu, P. Wang, J. Du, L. Wu, X. Li, Efficient region embedding with multi-view spatial networks: A perspective of locality-constrained spatial autocorrelations, in: Proc. AAAI, Vol. 33, 2019, pp. 906\u2013913.","DOI":"10.1609\/aaai.v33i01.3301906"},{"key":"10.1016\/j.inffus.2025.103464_b6","doi-asserted-by":"crossref","unstructured":"W. Chan, Q. Ren, Region-wise attentive multi-view representation learning for urban region embedding, in: Proc. CIKM, 2023, pp. 3763\u20133767.","DOI":"10.1145\/3583780.3615194"},{"key":"10.1016\/j.inffus.2025.103464_b7","doi-asserted-by":"crossref","unstructured":"Z. Li, W. Huang, K. Zhao, M. Yang, Y. Gong, M. Chen, Urban Region Embedding via Multi-View Contrastive Prediction, in: Proc. AAAI, vol. 38, 2024, pp. 8724\u20138732.","DOI":"10.1609\/aaai.v38i8.28718"},{"key":"10.1016\/j.inffus.2025.103464_b8","doi-asserted-by":"crossref","unstructured":"H. Wang, Z. Li, Region representation learning via mobility flow, in: Proc. CIKM, 2017, pp. 237\u2013246.","DOI":"10.1145\/3132847.3133006"},{"key":"10.1016\/j.inffus.2025.103464_b9","doi-asserted-by":"crossref","unstructured":"S. Wu, X. Yan, X. Fan, S. Pan, S. Zhu, C. Zheng, M. Cheng, C. Wang, Multi-graph fusion networks for urban region embedding, in: Proc. IJCAI, 2022, pp. 2312\u20132318.","DOI":"10.24963\/ijcai.2022\/321"},{"key":"10.1016\/j.inffus.2025.103464_b10","series-title":"Proc. ICML","first-page":"41151","article-title":"Spatial-temporal graph learning with adversarial contrastive adaptation","author":"Zhang","year":"2023"},{"issue":"10","key":"10.1016\/j.inffus.2025.103464_b11","doi-asserted-by":"crossref","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","article-title":"Spatio-temporal graph neural networks for predictive learning in urban computing: A survey","volume":"36","author":"Jin","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2025.103464_b12","first-page":"7526","article-title":"STP-TrellisNets+: Spatial-temporal parallel TrellisNets for multi-step metro station passenger flow prediction","volume":"35","author":"Ou","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2025.103464_b13","unstructured":"T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, in: Proc. ICLR, 2017, pp. 1\u201314."},{"key":"10.1016\/j.inffus.2025.103464_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113007","article-title":"Multi-view framework for multi-label bioactive peptide classification based on multi-modal representation learning","volume":"175","author":"Kang","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.inffus.2025.103464_b15","doi-asserted-by":"crossref","unstructured":"G. Teng, T. Mao, C. Shen, X. Tian, X. Liu, Y. Chen, J. Ye, URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering, in: Proc. SIGKDD, 2024, pp. 2888\u20132899.","DOI":"10.1145\/3637528.3671887"},{"key":"10.1016\/j.inffus.2025.103464_b16","doi-asserted-by":"crossref","unstructured":"Z. Yao, Y. Fu, B. Liu, W. Hu, H. Xiong, Representing urban functions through zone embedding with human mobility patterns, in: Proc. IJCAI, 2018, pp. 3919\u20133925.","DOI":"10.24963\/ijcai.2018\/545"},{"key":"10.1016\/j.inffus.2025.103464_b17","doi-asserted-by":"crossref","unstructured":"Y. Zhang, Y. Fu, P. Wang, X. Li, Y. Zheng, Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning, in: Proc. SIGKDD, 2019, pp. 1700\u20131708.","DOI":"10.1145\/3292500.3330972"},{"key":"10.1016\/j.inffus.2025.103464_b18","series-title":"Proc. AAAI","first-page":"1013","article-title":"Urban2vec: Incorporating street view imagery and pois for multi-modal urban neighborhood embedding","volume":"34","author":"Wang","year":"2020"},{"key":"10.1016\/j.inffus.2025.103464_b19","doi-asserted-by":"crossref","DOI":"10.1145\/3713070","article-title":"A survey of multimodal learning: Methods, applications, and future","author":"Yuan","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.inffus.2025.103464_b20","doi-asserted-by":"crossref","unstructured":"Y. Luo, F. lai Chung, K. Chen, Urban region profiling via multi-graph representation learning, in: Proc. CIKM, 2022, pp. 4294\u20134298.","DOI":"10.1145\/3511808.3557720"},{"key":"10.1016\/j.inffus.2025.103464_b21","doi-asserted-by":"crossref","unstructured":"M. Zhang, T. Li, Y. Li, P. Hui, Multi-view joint graph representation learning for urban region embedding, in: Proc. IJCAI, 2021, pp. 4431\u20134437.","DOI":"10.24963\/ijcai.2020\/611"},{"issue":"9","key":"10.1016\/j.inffus.2025.103464_b22","doi-asserted-by":"crossref","first-page":"9031","DOI":"10.1109\/TKDE.2022.3220874","article-title":"Region embedding with intra and inter-view contrastive learning","volume":"35","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.inffus.2025.103464_b23","series-title":"Proc. ICDE","first-page":"4409","article-title":"Urban region representation learning with attentive fusion","author":"Sun","year":"2024"},{"key":"10.1016\/j.inffus.2025.103464_b24","doi-asserted-by":"crossref","unstructured":"S. Zhou, D. He, L. Chen, S. Shang, P. Han, Heterogeneous region embedding with prompt learning, in: Proc. AAAI, Vol. 37, 2023, pp. 4981\u20134989.","DOI":"10.1609\/aaai.v37i4.25625"},{"key":"10.1016\/j.inffus.2025.103464_b25","doi-asserted-by":"crossref","unstructured":"Q. Zhang, C. Huang, L. Xia, Z. Wang, Z. Li, S. Yiu, Automated spatio-temporal graph contrastive learning, in: Proc. WWW, 2023, pp. 295\u2013305.","DOI":"10.1145\/3543507.3583304"},{"key":"10.1016\/j.inffus.2025.103464_b26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/BF02242925","article-title":"Criminality of place: Crime generators and crime attractors","volume":"3","author":"Brantingham","year":"1995","journal-title":"Eur. J. Crim. Policy Res."},{"key":"10.1016\/j.inffus.2025.103464_b27","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.inffus.2025.103464_b28","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"4672","article-title":"Mixture-of-linear-experts for long-term time series forecasting","author":"Ni","year":"2024"},{"key":"10.1016\/j.inffus.2025.103464_b29","article-title":"Time series prediction using mixtures of experts","volume":"9","author":"Zeevi","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.inffus.2025.103464_b30","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.ins.2021.12.085","article-title":"Adaptive dual-view wavenet for urban spatial\u2013temporal event prediction","volume":"588","author":"Jin","year":"2022","journal-title":"Inform. Sci."},{"key":"10.1016\/j.inffus.2025.103464_b31","first-page":"21","article-title":"Variational graph auto-encoders","volume":"1050","author":"Kipf","year":"2016","journal-title":"Stat"},{"key":"10.1016\/j.inffus.2025.103464_b32","doi-asserted-by":"crossref","unstructured":"J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, Line: Large-scale information network embedding, in: Proc. WWW, 2015, pp. 1067\u20131077.","DOI":"10.1145\/2736277.2741093"},{"key":"10.1016\/j.inffus.2025.103464_b33","doi-asserted-by":"crossref","unstructured":"A. Grover, J. Leskovec, node2vec: Scalable feature learning for networks, in: Proc. SIGKDD, 2016, pp. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"issue":"1","key":"10.1016\/j.inffus.2025.103464_b34","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.inffus.2025.103464_b35","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inform. Theory"},{"issue":"1","key":"10.1016\/j.inffus.2025.103464_b36","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"10.1016\/j.inffus.2025.103464_b37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.inffus.2025.103464_b38","doi-asserted-by":"crossref","unstructured":"T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proc. SIGKDD, 2016, pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"10.1016\/j.inffus.2025.103464_b39","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"2065","key":"10.1016\/j.inffus.2025.103464_b40","doi-asserted-by":"crossref","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci."}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253525005378?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253525005378?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T21:34:37Z","timestamp":1762551277000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253525005378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":40,"alternative-id":["S1566253525005378"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2025.103464","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-Scale Temporal Graph Contrastive Embedding for urban region representation","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2025.103464","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103464"}}