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Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>Community structure is a typical characteristic of complex networks. Finding communities in complex networks has many important applications, such as the advertisement and recommendation based on social networks and the discovery of new protein molecules in biological networks, which make it a hot topic in the field of complex network analysis. With the increasing concerns about the leakage of personal privacy, discovering communities spread across the local networks owned by multiple participants accurately while preserving each participant\u2019s privacy has become an emerging challenge in distributed community detection. In this article, we propose a general federated graph learning model for privacy-preserving distributed graph learning and develop two federated clique percolation algorithms (CPAs) based on it to discover overlapping communities distributed across multiple participants\u2019 local networks without disclosing any participant\u2019s network privacy. Homomorphic encryption and hash operation are used in combination to protect the privacy of the vertices and edges of each local network. Furthermore, vertex attributes are involved in the calculation of clique similarity and clique percolation when dealing with attributed networks. The experimental results on real-world and artificial datasets demonstrate that the proposed algorithms achieve identical results to those of their stand-alone counterparts and more than 200% higher accuracy than the simple distributed CPAs without federating learning.<\/jats:p>","DOI":"10.1145\/3604807","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T15:36:11Z","timestamp":1687188971000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Federated Clique Percolation for Privacy-preserving Overlapping Community Detection"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6270-2468","authenticated-orcid":false,"given":"Kun","family":"Guo","sequence":"first","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6586-4588","authenticated-orcid":false,"given":"Wenzhong","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-297X","authenticated-orcid":false,"given":"Enjie","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0302-4510","authenticated-orcid":false,"given":"Yutong","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3374-2991","authenticated-orcid":false,"given":"Jiachen","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-3295","authenticated-orcid":false,"given":"Ximeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-6028","authenticated-orcid":false,"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Hong Kong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32063-7_47"},{"key":"e_1_3_3_3_2","doi-asserted-by":"crossref","unstructured":"Xiaoyan Bi Tie Qiu Wenyu Qu Laiping Zhao Xiaobo Zhou and Dapeng Oliver Wu. 2020. 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