Abstract
Nowadays there is a boom in social network data streaming from various fields of interest related to finance, engineering, medicine, and general sciences. All these data are modeled as graphs for better analysis. Community detection is one such mechanism for the analysis of such massive data. Many community detection algorithms exist in literature. The existing algorithms are compared by using either real-world or artificial networks (modeled as graphs) but not both. This paper aims to make a comparative study of two popular existing community detection algorithms both on real-world and synthetic data and verify their performance. The approach in this paper makes good use of recent advances in graphical modeling of different social networks. We generated a random graph that represents most of the observed properties of a real-world dataset. The experimental results are tabulated and the computed metrics help in inferring the suitability or scalability of an algorithm for small or massive datasets.
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Rajita, B.S.A.S., Kumari, D., Panda, S. (2020). A Comparative Analysis of Community Detection Methods in Massive Datasets. In: Goel, N., Hasan, S., Kalaichelvi, V. (eds) Modelling, Simulation and Intelligent Computing. MoSICom 2020. Lecture Notes in Electrical Engineering, vol 659. Springer, Singapore. https://doi.org/10.1007/978-981-15-4775-1_19
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DOI: https://doi.org/10.1007/978-981-15-4775-1_19
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