Structure-aware multi-view urban representation learning with coordinated fusion and alignment
文献类型：期刊论文
| 作者 | Wei, Jinghui1,3; Wu, Sheng2,3; Cheng, Shifen4,5; Wang, Peixiao4,5; Lu, Feng3,4,5 |
| 刊名 | INFORMATION PROCESSING & MANAGEMENT
 |
| 出版日期 | 2026-07-01 |
| 卷号 | 63期号:5页码:104672 |
| 关键词 | Urban representation learning Multi-view data fusion Contrastive learning Embedding Coordinated optimization |
| ISSN号 | 0306-4573 |
| DOI | 10.1016/j.ipm.2026.104672 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Urban representation learning leverages heterogeneous data. While unified frameworks that combine feature fusion and contrastive learning have achieved promising results, two key challenges remain: 1) the lack of structural awareness often leads to suboptimal negative sampling and reduces the discriminability of embeddings; and 2) the inherently conflicting optimization objectives between fusion and contrastive modules may result in unstable training and suboptimal convergence. To address these issues, we propose SAMC, a Structure-Aware Multi-view representation learning framework with Coordinated fusion and alignment. SAMC introduces a multi-view contrastive learning module that incorporates structural similarity into negative sampling, thereby enhancing semantic coherence and cross-view consistency. To improve training stability, a flexible optimization strategy that incorporates soft Lagrangian constraints and stepwise state tracking is designed to coordinate gradient updates across fusion and alignment modules. Experiments on multiple urban datasets show that SAMC achieves average improvements of approximately 17.2%, 14.2%, and 5.9% compared to the state-of-the-art baselines on the tasks of regional popularity prediction, service demand prediction, and land use classification, respectively. Visualization analyses further confirm that SAMC enhances the discriminability, robustness, and generalizability of urban representations by embedding structural priors and adopting multi-stage coordinated optimization. Moreover, SAMC achieves a favorable tradeoff between computational efficiency and predictive performance. |
| URL标识 | 查看原文 |
| WOS研究方向 | Computer Science ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001686505800001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/220940]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Cheng, Shifen |
| 作者单位 | 1.Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350108, Peoples R China; 2.Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350108, Peoples R China; 3.Fuzhou Univ, Acad Digital China Fujian, Fuzhou 350108, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wei, Jinghui,Wu, Sheng,Cheng, Shifen,et al. Structure-aware multi-view urban representation learning with coordinated fusion and alignment[J]. INFORMATION PROCESSING & MANAGEMENT,2026,63(5):104672. |
| APA | Wei, Jinghui,Wu, Sheng,Cheng, Shifen,Wang, Peixiao,&Lu, Feng.(2026).Structure-aware multi-view urban representation learning with coordinated fusion and alignment.INFORMATION PROCESSING & MANAGEMENT,63(5),104672. |
| MLA | Wei, Jinghui,et al."Structure-aware multi-view urban representation learning with coordinated fusion and alignment".INFORMATION PROCESSING & MANAGEMENT 63.5(2026):104672. |
入库方式： OAI收割
来源：地理科学与资源研究所
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