Bayesian approach to network modularity
Bayesian approach to network modularity
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DOI：
10.1103/physrevlett.100.258701
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发表时间：
2008-06-27
影响因子：
8.6
通讯作者：
Wiggins, Chris H.
中科院分区：
文献类型：
--
作者：
Hofman, Jake M.;Wiggins, Chris H.
我们提出了一个有效的，原则性的，和可解释的技术，用于推断模块分配，并确定在一个给定的网络中的模块的最佳数量。我们将展示如何找到模块的几种现有方法可以被描述为我们工作的变体，特殊或限制情况，以及该方法如何克服分辨率限制问题，准确地恢复模块的真实数量。我们的方法是基于贝叶斯方法的模型选择已成功地使用了近世纪，实现了使用变分技术，仅在过去的十年中发展。我们将该技术应用于合成和真实的网络，并概述了该方法如何自然地允许竞争模型之间的选择。
We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem, accurately recovering the true number of modules. Our approach is based on Bayesian methods for model selection which have been used with success for almost a century, implemented using a variational technique developed only in the past decade. We apply the technique to synthetic and real networks and outline how the method naturally allows selection among competing models.