Resolution limit in community detection
Resolution limit in community detection
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DOI：
10.1073/pnas.0605965104
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发表时间：
2007-01-02
影响因子：
11.1
通讯作者：
Barthelemy, Marc
中科院分区：
文献类型：
--
作者：
Fortunato, Santo;Barthelemy, Marc
发现社区结构对于揭示复杂网络中结构与功能之间的联系以及在生物学和社会学等许多学科中的实际应用至关重要。现在广泛使用的一种流行方法依赖于称为模块化的数量的优化，模块化是将网络划分为社区的质量指标。我们发现，模块化优化可能无法识别模块小于一个规模，这取决于网络的总规模和模块的互连程度，即使在模块明确定义的情况下。这一发现通过几个例子得到了证实，无论是在人工和真实的社会，生物和技术网络中，我们表明，模块化优化确实没有解决大量的模块。因此，通过模块化优化获得的模块的检查是必要的，我们在这里提供的社区检测方法的可靠性评估的关键要素。
Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.