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Electronics 2019, 8(1), 23;

On the Analysis of the Influence of the Evaluation Metric in Community Detection over Social Networks

Department Computer Sciences, Universidad Rey Juan Carlos, 28933 Móstoles, Spain
Author to whom correspondence should be addressed.
Received: 18 October 2018 / Revised: 3 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
(This article belongs to the Section Computer Science & Engineering)
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Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps with analyzing groups of users with similar interests. As a consequence, it is possible to detect radicalism or even reduce the size of the data to be analyzed, among other applications. This paper presents a metaheuristic approach based on Greedy Randomized Adaptive Search Procedure (GRASP) methodology for detecting communities in social networks. The community detection problem is modeled as an optimization problem, where the objective function to be optimized is the modularity of the network, a well-known metric in this scientific field. The results obtained outperform classical methods of community detection over a set of real-life instances with respect to the quality of the communities detected. View Full-Text
Keywords: social network; community detection; metaheuristic; optimization; GRASP social network; community detection; metaheuristic; optimization; GRASP

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Pérez-Peló, S.; Sánchez-Oro, J.; Martín-Santamaría, R.; Duarte, A. On the Analysis of the Influence of the Evaluation Metric in Community Detection over Social Networks. Electronics 2019, 8, 23.

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