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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Representation learning of road networks is essential for various downstream traffic-related tasks, as road network contain multi-modal data with rich information, and the learned embeddings can be directly used in machine learning models. However, due to the dynamic changes in road networks with respect to topology and associated data, as well as the local and long-range dependency caused by complex mobility semantics, learning robust and effective representations remains challenging. To this end, we exploit the properties of the road network and the mobility semantics embedded in trajectories, and propose a novel\n            <jats:italic toggle=\"yes\">S<\/jats:italic>\n            emantic-\n            <jats:italic toggle=\"yes\">E<\/jats:italic>\n            nhanced\n            <jats:italic toggle=\"yes\">G<\/jats:italic>\n            raph\n            <jats:italic toggle=\"yes\">C<\/jats:italic>\n            ontrastive\n            <jats:italic toggle=\"yes\">L<\/jats:italic>\n            earning (SE-GCL) framework, for learning general-purpose embeddings of road networks. Specifically, in this framework, we propose (1) a multi-modal feature embedding module to capture both the attribute and visual information of road segments, (2) a semantic-enhanced graph augmentation strategy to simulate topological changes and data missing in the road network, and (3) a semantic-enhanced contrastive optimization module that leverages geo-locality and mobility semantics to guide representation learning. Extensive experiments are conducted on two real-world road networks with three representative downstream tasks. The result demonstrate that SE-GCL yields more robust and effective representations, outperforming the state-of-the-art baselines. The source code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/csjiezhao\/SE-GCL\">https:\/\/github.com\/csjiezhao\/SE-GCL<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3757921","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T15:01:12Z","timestamp":1754319672000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SE-GCL: A Semantic-Enhanced Graph Contrastive Learning Framework for Road Network Embedding"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0039-6220","authenticated-orcid":false,"given":"Jie","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China and Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2094-9734","authenticated-orcid":false,"given":"Chao","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China and College of Computer and Data Science, Fuzhou University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3952-0826","authenticated-orcid":false,"given":"Wanyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7687-2162","authenticated-orcid":false,"given":"Mingyu","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9830-3955","authenticated-orcid":false,"given":"Huayan","family":"Pu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-5631","authenticated-orcid":false,"given":"Jun","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"144","volume-title":"Proceedings of the 26th International Conference on Extending Database Technology (EDBT), Vol","volume":"26","author":"Chang Yanchuan","year":"2023","unstructured":"Yanchuan Chang, Egemen Tanin, Xin Cao, and Jianzhong Qi. 2023. 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