Abstract
Community within the social networks is demarcated as collection of nodes which are thickly associated among them whereas sparingly associated to nodes exterior to the community. The problem of community detection is a key topic of research and has gained enough attention over last two decades. Considering the case of Dynamic Social Networks those changes with time community detection is an important issue. The existence of overlapping communities has complicated the task of community detection. In this review work, the authors have illustrated a few community detection algorithms that discover communities within dynamic social networks. An analysis of the discussed algorithms with a focus on the further research work has been done and a real life examples have been cited in the paper.
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The authors do hereby declare that there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Paul, S., Koner, C., Mitra, A., Ghosh, S. (2024). A Study on Algorithms for Detection of Communities in Dynamic Social Networks: A Review. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1956. Springer, Cham. https://doi.org/10.1007/978-3-031-48879-5_5
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