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A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user\u2019s attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user\u2019s activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks.<\/jats:p>","DOI":"10.1145\/3650179","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T12:10:09Z","timestamp":1709295009000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Fuzzy Influence Maximization in Social Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2081-8112","authenticated-orcid":false,"given":"Ahmad","family":"Zareie","sequence":"first","affiliation":[{"name":"Department of Computer Science, The University of Manchester\rManchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6104-6649","authenticated-orcid":false,"given":"Rizos","family":"Sakellariou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Manchester\rManchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2480362.2480389"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01286-2"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124368"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/2634074.2634144"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3003047"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835934"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3045783"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118066"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep02980"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502525"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718518"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/1963192.1963217"},{"issue":"3","key":"e_1_3_1_15_2","article-title":"Influence maximization revisited: Efficient sampling with bound tightened","volume":"47","author":"Guo Qintian","year":"2022","unstructured":"Qintian Guo, Sibo Wang, Zhewei Wei, Wenqing Lin, and Jing Tang. 2022. Influence maximization revisited: Efficient sampling with bound tightened. ACM Trans. Datab. Syst. 47, 3 (2022), 45.","journal-title":"ACM Trans. Datab. Syst."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2022.3141952"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.12.091"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjds5"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03430-6"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_3_1_21_2","first-page":"906","volume-title":"Proceedings of the International Conference on Computational Science and its Applications","author":"Kumari Anisha","year":"2020","unstructured":"Anisha Kumari, Ranjan Kumar Behera, Abhishek Sai Shukla, Satya Prakash Sahoo, Sanjay Misra, and Sanatanu Kumar Rath. 2020. Quantifying influential communities in granular social networks using fuzzy theory. 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