ISSN: 2222-6990
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Community detection in complex networks is widely used across fields, from sociology to biology. Despite its broad applications, there has been limited exploration of real-world experiments involving the use of datasets, algorithms, software, and openly shared code resources. This review addresses this gap by analyzing 88 publications from 2017 to 2023, focusing on the practical implementation of community detection methods. A thematic review (TR) process using ATLAS.ti 23 identifies six key themes: Healthcare, Social Networks, Telecommunications, Economics, Intelligence, and Natural Sciences. The review provides insights into the prevalent use of algorithms, notably the Louvain method, and evaluates their effectiveness across different domains. It also highlights challenges faced in real-world applications, such as scalability, accuracy, and domain-specific limitations. By offering a comprehensive overview of tools, techniques, and resources, this review aims to guide researchers and practitioners in understanding and applying community detection effectively in various real-world contexts.
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