Discovering communities is one of the important features of complex networks, as it reveals the structural features within such networks. Community detection is an optimization problem, and there have been significant efforts devoted to detecting communities with dense intra-links. However, single-objective optimization approaches are inadequate for complex networks. In this work, we propose the Multi-Layer Ant Colony Optimization (MLACO) to detect communities in complex networks. This algorithm takes Ratio Cut (RC) and Kernel K-means (KKM) as an objective function and attempts to find the optimal solution. The findings from our evaluation of MLACO using both synthetic and real-world complex networks demonstrate that it outperforms other competing approaches, in terms of normalized mutual information (NMI) and modularity (Q). Moreover, we also evaluate our algorithm for small-scale and large-scale networks showing the utility of our proposed approach.

中文翻译：

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使用多层蚁群优化从不相交的复杂网络中发现社区

发现社区是复杂网络的重要特征之一，因为它揭示了此类网络内的结构特征。社区检测是一个优化问题，人们在检测具有密集内部链接的社区方面付出了巨大的努力。然而，单目标优化方法不足以应对复杂的网络。在这项工作中，我们提出了多层蚁群优化（MLACO）来检测复杂网络中的社区。该算法以Ratio Cut（RC）和Kernel K-means（KKM）为目标函数，尝试寻找最优解。我们使用合成和现实世界的复杂网络对 MLACO 进行评估的结果表明，它在归一化互信息 (NMI) 和模块化 (Q) 方面优于其他竞争方法。此外，我们还针对小规模和大规模网络评估了我们的算法，显示了我们提出的方法的实用性。