{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:15:32Z","timestamp":1772788532146,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>Given a graph G and a query node q, community search (CS) seeks a cohesive subgraph from G that contains q. CS has gained much research interests recently. In the database research community, researchers aim to find the most cohesive subgraph satisfying a specific community model (e.g., k-core or k-truss) via graph traversal. These works obtain good precision, however suffering from the low efficiency issue. In the AI research community, a new thought of using the deep learning model to support CS without relying on graph traversal emerges. Supervised end-to-end models using GCN are presented, which perform efficiently, but leave a large room for precision improvement. None of them can achieve a good balance between the efficiency and effectiveness. This motivates our solution: First, we present an offline community-injected graph embedding method to preserve the community\u2019s cohesiveness features into the learned node representations. Second, we resort to a proximity graph (PG) built from node representations, to quickly return the community online. Moreover, we develop a self-augmented method based on KL divergence to further optimize node representations. Extensive experiments on seven real-world graphs show our solution\u2019s superiority on effectiveness (at least 39.3% improvement) and efficiency (one to two orders of magnitude faster).<\/jats:p>","DOI":"10.3233\/faia230358","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:10:49Z","timestamp":1695978649000},"source":"Crossref","is-referenced-by-count":3,"title":["Effective and Efficient Community Search with Graph Embeddings"],"prefix":"10.3233","author":[{"given":"Xiaoxuan","family":"Gou","sequence":"first","affiliation":[{"name":"Hangzhou Dianzi University, School of Computer Science, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, School of Computer Science, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangying","family":"Wu","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, School of Computer Science, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runhuai","family":"Chen","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, School of Computer Science, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, School of Computer Science, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxing","family":"Wu","sequence":"additional","affiliation":[{"name":"Southeast University, School of Computer Science and Engineering, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Ke","sequence":"additional","affiliation":[{"name":"Zhejiang University, School of Software Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230358","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:10:51Z","timestamp":1695978651000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230358"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230358","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}