Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practitioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive, and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimization schemes as well as a comparison of their partitioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

中文翻译：

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社区结构：社区检测方法的比较评估

发现复杂网络中的社区结构是一个成熟的领域，因为文献中已经引入了大量的社区检测方法。尽管如此，对于从业者来说，确定哪种方法适合深入了解他们研究的网络的结构信息仍然是非常具有挑战性的。最近许多努力致力于调查社区结构的各种质量分数，但区分不同类型社区的问题仍然悬而未决。在本文中，我们提出了一项比较、广泛和实证的研究，以调查许多最先进和知名的社区检测方法正在产生哪些类型的社区。具体来说，我们对计算时间、社区规模分布、根据优化方案对方法进行比较评估，并通过验证指标比较它们的划分策略。我们对来自五个不同网络类别的数百个网络的庞大语料库进行分析，并提出对社区检测方法进行分类的方法，帮助潜在用户在复杂的社区检测环境中导航。