Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyzing groups of users with similar interests. This task is also useful in different areas, such as biology (interactions of genes and proteins), psychology (diagnostic criteria), or criminology (fraud detection). This paper presents a metaheuristic approach based on Variable Neighborhood Search (VNS) which leverages the combination of quality and diversity of a constructive procedure inspired in Greedy Randomized Adaptative Search Procedure (GRASP) for detecting communities in social networks. In this work, the community detection problem is modeled as a bi-objective optimization problem, where the two objective functions to be optimized are the Negative Ratio Association (NRA) and Ratio Cut (RC), two objectives that have already been proven to be in conflict. To evaluate the quality of the obtained solutions, we use the Normalized Mutual Information (NMI) metric for the instances under evaluation whose optimal solution is known, and modularity for those in which the optimal solution is unknown. Furthermore, we use metrics widely used in multi-objective optimization community to evaluate solutions, such as coverage, $\epsilon$-indicator, hypervolume, and inverted generational distance. The obtained results outperform the state-of-the-art method for community detection over a set of real-life instances in both, quality and computing time.

社交网络中的社区检测正成为社交网络分析的关键任务之一，因为它有助于分析具有相似兴趣的用户组。此任务在不同领域也很有用，例如生物学（基因和蛋白质的相互作用）、心理学（诊断标准）或犯罪学（欺诈检测）。本文提出了一种基于可变邻域搜索 (VNS) 的元启发式方法，该方法利用了受贪婪随机自适应搜索程序 (GRASP) 启发的构造程序的质量和多样性的组合，用于检测社交网络中的社区。在这项工作中，社区检测问题被建模为一个双目标优化问题，其中要优化的两个目标函数是负比率关联（NRA）和比率削减（RC），已经证明存在冲突的两个目标。为了评估获得的解决方案的质量，我们对最优解已知的评估实例使用归一化互信息（NMI）度量，对最优解未知的实例使用模块化。此外，我们使用在多目标优化社区中广泛使用的指标来评估解决方案，例如覆盖率、$\epsilon$- 指标、超容量和反向代际距离。获得的结果在质量和计算时间上都优于一组现实生活实例中最先进的社区检测方法。