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ACM Manag. Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"<jats:p>\n                    As the state-of-the-art methods for high-dimensional data retrieval, Approximate Nearest Neighbor Search (ANNS) approaches with graph-based indexes have attracted increasing attention and play a crucial role in many real-world applications, e.g.,\n                    <jats:italic toggle=\"yes\">retrieval-augmented generation<\/jats:italic>\n                    (RAG) and recommendation systems. Unlike the extensive works focused on designing efficient graph-based ANNS methods, this paper delves into merging multiple existing graph-based indexes into a single one, which is also crucial in many real-world scenarios (e.g., cluster consolidation in distributed systems and read-write contention in real-time vector databases). We propose a Fast Graph-based Indexes Merging (FGIM) framework with three core techniques: (1)\n                    <jats:italic toggle=\"yes\">Proximity Graphs (PGs)<\/jats:italic>\n                    to\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    <jats:italic toggle=\"yes\">Nearest Neighbor Graph<\/jats:italic>\n                    (\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    -NNG) transformation used to extract potential candidate neighbors from input graph-based indexes through\n                    <jats:italic toggle=\"yes\">cross-querying,<\/jats:italic>\n                    (2)\n                    <jats:italic toggle=\"yes\">k-NNG refinement<\/jats:italic>\n                    designed to identify overlooked high-quality neighbors and maintain graph connectivity, and (3)\n                    <jats:italic toggle=\"yes\">k<\/jats:italic>\n                    -\n                    <jats:italic toggle=\"yes\">NNG to PG transformation<\/jats:italic>\n                    aimed at improving graph navigability and enhancing search performance. Then, we integrate our FGIM framework with the state-of-the-art ANNS method, HNSW, and other existing mainstream graph-based methods to demonstrate its generality and merging efficiency. Extensive experiments on six real-world datasets show that our FGIM framework is applicable to various mainstream graph-based ANNS methods, achieves up to 3.5\u00d7 speedup over HNSW's incremental construction and an average of 7.9\u00d7 speedup for methods without incremental support, while maintaining comparable or superior search performance.\n                  <\/jats:p>","DOI":"10.1145\/3786651","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["FGIM: a Fast Graph-based Indexes Merging Framework for Approximate Nearest Neighbor Search"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8175-3508","authenticated-orcid":false,"given":"Zekai","family":"Wu","sequence":"first","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2078-3771","authenticated-orcid":false,"given":"Jiabao","family":"Jin","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9797-6944","authenticated-orcid":false,"given":"Peng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6303-198X","authenticated-orcid":false,"given":"Xiaoyao","family":"Zhong","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8257-5806","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"HKUST (GZ), Guangzhou, China and HKUST, Hong Kong SAR, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5598-0312","authenticated-orcid":false,"given":"Yongxin","family":"Tong","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4015-126X","authenticated-orcid":false,"given":"Zhitao","family":"Shen","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2549-8322","authenticated-orcid":false,"given":"Jingkuan","family":"Song","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2999-2088","authenticated-orcid":false,"given":"Heng Tao","family":"Shen","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-7225","authenticated-orcid":false,"given":"Xuemin","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Jiaotong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Ant Group 2025. 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