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In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities.<\/jats:p>","DOI":"10.1002\/dac.5501","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T05:19:58Z","timestamp":1682659198000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Community detection and service discovery on Social Internet of Things"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2575-8697","authenticated-orcid":false,"given":"Mozhgan","family":"Malekshahi Rad","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Science and Research Branch Islamic Azad University  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir 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