There is increasing appetite for analysing multiple network data. This is different to analysing traditional data sets, where now each observation in the data comprises a network. Recent technological advancements have allowed the collection of this type of data in a range of different applications. This has inspired researchers to develop statistical models that most accurately describe the probabilistic mechanism that generates a network population and use this to make inferences about the underlying structure of the network data. Only a few studies developed to date consider the heterogeneity that can exist in a network population. We propose a Mixture of Measurement Error Models for identifying clusters of networks in a network population, with respect to similarities detected in the connectivity patterns among the networks' nodes. Extensive simulation studies show our model performs well in both clustering multiple network data and inferring the model parameters. We further apply our model on two real world multiple network data sets resulting from the fields of Computing (Human Tracking Systems) and Neuroscience.
翻译:分析多网络数据的胃口越来越大。这与分析传统数据集不同,现在数据中的每组观察都包括一个网络。最近的技术进步使得能够在不同的应用中收集这类数据。这激励了研究人员开发统计模型,最准确地描述产生网络人口的概率机制,并以此来推断网络数据的基本结构。迄今为止,只有少数研究考虑了网络人口中可能存在的异质性。我们提出了计量错误模型混合体,用以识别网络人口中的网络群,以及网络节点之间连接模式所发现的相似点。广泛的模拟研究表明,我们的模型在组合多网络数据和推断模型参数方面表现良好。我们进一步将模型应用于来自计算机(人类跟踪系统)和神经科学领域的两个真实世界多网络数据集。