Abstract
We focus in this paper on the identification of groups of members who share similar tastes and preferences in the context of social networks. We address the need for community detection based on two different approaches: the first one is graph-oriented, while the second one concerns an optimized classification algorithm. The graph oriented approach is based on the calculation of the cycles and considers two variants: the modularity computation and the external links. The optimized classification approach applies the K-means algorithm as an initial classification solution, and then optimizes this classification by using two different meta-heuristics: the Tabu Search (employing local search methods to explore the solution space beyond local optimality) and the Bee Swarm Optimization algorithm (a population-based search algorithm). In order to validate our proposal, experiments were conducted on different datasets. The results obtained are promising in terms of modularity and response time.
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Berkani, L., Madani, S., Mekherbeche, S. (2019). Optimized Community Detection in Social Networks. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) New Knowledge in Information Systems and Technologies. WorldCIST'19 2019. Advances in Intelligent Systems and Computing, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_50
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DOI: https://doi.org/10.1007/978-3-030-16184-2_50
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