论文标题
重新审视的分辨率限制：使用广义模块化密度的社区检测
Resolution limit revisited: community detection using generalized modularity density
论文作者
论文摘要
近年来，通过考虑检测算法中模块化指标的变体来解决社区检测中解决分辨率限制问题（RL）问题。据称，这些指标在很大程度上减轻了RL问题，并且在许多现实情况下比模块化更可取。但是，它们通常不适合分析加权网络或检测层次结构结构。但是，解决方案限制问题可能很复杂，尤其是不清楚何时应该将其视为问题。在本文中，我们介绍了一个指标，我们称之为广义模块化密度$ q_g $，该度量可以消除任何所需分辨率的RL问题，并且很容易扩展以研究加权和层次结构网络。我们还提出了一个基准测试，以量化分辨率限制问题，检查各种模块化式指标，以表明新的度量$ q_g $表现最好，并表明$ q_g $可以识别现实世界中的模块化结构和否则将隐藏的人造网络中的模块化结构。
Various attempts have been made in recent years to solve the Resolution Limit (RL) problem in community detection by considering variants of the modularity metric in the detection algorithms. These metrics purportedly largely mitigate the RL problem and are preferable to modularity in many realistic scenarios. However, they are not generally suitable for analyzing weighted networks or for detecting hierarchical community structure. Resolution limit problems can be complicated, though, and in particular it can be unclear when it should be considered as problem. In this paper, we introduce a metric that we call generalized modularity density $Q_g$ that eliminates the RL problem at any desired resolution and is easily extendable to study weighted and hierarchical networks. We also propose a benchmark test to quantify the resolution limit problem, examine various modularity-like metrics to show that the new metric $Q_g$ performs best, and show that $Q_g$ can identify modular structure in real-world and artificial networks that is otherwise hidden.
