Clustering of heterogeneous populations of networks
Clustering of heterogeneous populations of networks
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DOI：
10.1103/physreve.105.014312
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Jean-Gabriel Young;Alec Kirkley;M. Newman
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文献类型：
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作者：
Jean-Gabriel Young;Alec Kirkley;M. Newman
用于从重复测量重构网络的统计方法通常假设所有测量都是从相同的底层网络结构生成的。然而，情况不必如此。例如，人们的社交网络在工作日和周末可能是不同的。大脑网络可能在健康患者和痴呆症或其他疾病患者之间存在差异。在这里，我们描述了一个贝叶斯分析框架，这样的数据，允许的事实，网络测量可能是反映多个可能的结构。我们定义了一个有限的混合模型的测量过程，并推导出一个吉布斯抽样程序，完全从模型参数的后验分布的样本。最终结果是将测量的网络聚类成具有相似结构的组。我们证明了真实的和合成网络人口的方法。
Statistical methods for reconstructing networks from repeated measurements typically assume that all measurements are generated from the same underlying network structure. This need not be the case, however. People's social networks might be different on weekdays and weekends, for instance. Brain networks may differ between healthy patients and those with dementia or other conditions. Here we describe a Bayesian analysis framework for such data that allows for the fact that network measurements may be reflective of multiple possible structures. We define a finite mixture model of the measurement process and derive a Gibbs sampling procedure that samples exactly from the full posterior distribution of model parameters. The end result is a clustering of the measured networks into groups with similar structure. We demonstrate the method on both real and synthetic network populations.