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In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.<\/jats:p>","DOI":"10.3233\/jifs-200563","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T15:57:28Z","timestamp":1597161448000},"page":"1597-1608","source":"Crossref","is-referenced-by-count":2,"title":["A novel genetic algorithm-based improvement model for online communities and trust networks"],"prefix":"10.1177","volume":"40","author":[{"given":"Ilker","family":"Bekmezci","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, MEF University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murat","family":"Ermis","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Istanbul Kultur University, Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egemen 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