{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:58:31Z","timestamp":1786089511742,"version":"3.56.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Using multi-modal data to learn region representations has gained popularity for its ability to reveal diverse socioeconomic features in cities. However, many studies focus solely on semantic features from points-of-interest (POIs), neglecting the issue of spatial imbalance. This article introduces a Multi-Graph Representation Learning framework for Region Embedding (MGRL4RE), which leverages both inter-region and intra-region correlations through two main components: multi-graph construction based on various region correlations and multi-graph representation learning. The construction module creates a multi-graph reflecting various correlations among regions, utilizing geo-tagged POIs, region data, and human mobility data. Specifically, we assess a region\u2019s importance relative to its spatial context (neighborhood) and develop spatially invariant semantic features to address spatial imbalance. Furthermore, the representation learning module generates comprehensive and effective region representations\n            <jats:italic>via<\/jats:italic>\n            multi-view embedding fusion. Our extensive experiments across various downstream tasks, including land use clustering, region popularity prediction, and crime prediction, confirm that our model significantly outperforms existing state-of-the-art region embedding methods.\n          <\/jats:p>","DOI":"10.1145\/3712698","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T17:20:48Z","timestamp":1737393648000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["MGRL4RE: A Multi-Graph Representation Learning Approach for Urban Region Embedding"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6633-9205","authenticated-orcid":false,"given":"Meng","family":"Chen","sequence":"first","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3093-3039","authenticated-orcid":false,"given":"Zechen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4675-7451","authenticated-orcid":false,"given":"Hongwei","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7107-5948","authenticated-orcid":false,"given":"Xin","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7390-8220","authenticated-orcid":false,"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenzhen Data Management Center of Planning and Natural Resources, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4415-0317","authenticated-orcid":false,"given":"Qiang","family":"Gao","sequence":"additional","affiliation":[{"name":"State Grid Jining Power Supply Company, Jining, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6903-4933","authenticated-orcid":false,"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Shandong Women\u2019s University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-1294","authenticated-orcid":false,"given":"Yilong","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","author":"Belkin Mikhail","year":"2001","unstructured":"Mikhail Belkin and Partha Niyogi. 2001. 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