Robust detection of dynamic community structure in networks
Robust detection of dynamic community structure in networks
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DOI：
10.1063/1.4790830
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发表时间：
2013-03-01
期刊：
影响因子：
2.9
通讯作者：
Mucha, Peter J.
中科院分区：
文献类型：
--
作者：
Bassett, Danielle S.;Porter, Mason A.;Mucha, Peter J.
我们描述了在某些类型的时间依赖网络中稳健检测社区结构的技术。具体地说，我们考虑使用统计零模型来促进半可分解系统中结构模块的原则性识别。零模型在诸如模块化等质量函数的优化以及随后对所识别的社区结构的统计有效性的评估中都发挥着重要作用。我们考察了这些方法对模型参数的敏感性，并展示了与零模型的比较如何有助于识别系统规模。通过考虑大量的优化，我们量化了网络诊断相对于优化的方差(“优化方差”)和网络结构的过度随机化(“随机化方差”)。由于模块化质量函数对于使用真实数据构建的网络通常具有大量近乎退化的局部最优，因此我们开发了一种构造代表性分区的方法，该方法使用零模型来校正分区集中的统计噪声。为了说明我们的结果，我们使用了从非线性振荡器和经验神经科学数据中提取的时间依赖网络的集合。(C)2013年美国物理研究所。[http://dx.doi.org/10.1063/1.4790830]
We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations ("optimization variance") and over randomizations of network structure ("randomization variance"). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4790830]