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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Resolution limit in community detection
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Resolution limit in community detection

机译：社区检测中的分辨率限制

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摘要

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.
机译：检测社区结构对于发现复杂网络中结构与功能之间的联系以及生物学和社会学等许多学科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对称为模块性的数量的优化，这是将网络划分为社区的质量指标。我们发现，即使在模块定义明确的情况下，模块化优化也可能无法识别小于规模的模块，该模块的规模取决于网络的总大小和模块的互连程度。通过在人工的和实际的社会，生物学和技术网络中的几个示例，可以证实这一发现，在这些示例中，我们表明模块化优化确实不能解决大量模块。因此，有必要对通过模块化优化获得的模块进行检查，在此我们为评估该社区检测方法的可靠性提供了关键要素。

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