适用于无分辨率限制的社区检测的窄范围
Narrow scope for resolution-limit-free community detection
摘要：
近年来，大型网络中的社区检测引起了人们的广泛关注。虽然模块化仍然是社区检测的最流行的方法之一，但所谓的分辨率限制仍然是一个显著的缺点。为了克服这个问题，最近有人建议，与其将网络与随机零模型进行比较，不如将其与常数因子进行比较。然而，目前尚不清楚“无分辨率限制”的确切含义，即不受分辨率限制的影响。此外，问题仍然是哪些其他方法可以被归类为无分辨率限制。本文给出了无分辨限方法的严格定义，并导出了无分辨限方法的一些基本性质。更重要的是，我们能够确切地证明哪一类社区检测方法是无分辨率限制的。此外，我们分析了哪些方法不是无分辨率限制的，这表明无分辨率限制的社区检测方法的范围有限。最后，我们提供了这样一个自然的配方，并显示它表现出色。
英文摘要：
Detecting communities in large networks has drawn much attention over the years. While modularity remains one of the more popular methods of community detection, the so-called resolution limit remains a significant drawback. To overcome this issue, it was recently suggested that instead of comparing the network to a random null model, as is done in modularity, it should be compared to a constant factor. However, it is unclear what is meant exactly by "resolution-limit-free", that is, not suffering from the resolution limit. Furthermore, the question remains what other methods could be classified as resolution-limit-free. In this paper we suggest a rigorous definition and derive some basic properties of resolution-limit-free methods. More importantly, we are able to prove exactly which class of community detection methods are resolution-limit-free. Furthermore, we analyze which methods are not resolution-limit-free, suggesting there is only a limited scope for resolution-limit-free community detection methods. Finally, we provide such a natural formulation, and show it performs superbly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
