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Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community-enhanced framework is presented in this article to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using parameterized influence regions of nodes and their contribution in community partitions. Then, a unique feature set is generated using local, global, and quasi-local similarity-based as well as community information-based features. This feature set is further optimized using scoring-based feature selection methods to select only the most relevant features. Finally, four machine learning-based classification models are used for link prediction. The experiments are performed on six well-known dynamic networks and three performance metrics, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3580513","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T12:04:53Z","timestamp":1674561893000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Community-enhanced Link Prediction in Dynamic Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2117-8660","authenticated-orcid":false,"given":"Mukesh","family":"Kumar","sequence":"first","affiliation":[{"name":"Indian Institute of Technology (BHU), India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6429-3063","authenticated-orcid":false,"given":"Shivansh","family":"Mishra","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (BHU), India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-2258","authenticated-orcid":false,"given":"Shashank Sheshar","family":"Singh","sequence":"additional","affiliation":[{"name":"Thapar Institute of Engineering and Technology, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9762-3834","authenticated-orcid":false,"given":"Bhaskar","family":"Biswas","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology (BHU), India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1134271.1134284"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/1134271.1134277"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/e2008-00425-1"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2017.9020002"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/1390681.1442798"},{"key":"e_1_3_2_7_2","first-page":"798","volume-title":"Proceedings of the Workshop on Link Analysis, Counter-terrorism and Security (SDM\u201906)","volume":"30","author":"Hasan Mohammad Al","year":"2006","unstructured":"Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed Zaki. 2006. 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