Actor–event data are common in sociological settings, whereby one registers the pattern of attendance of a group of social actors to a number of events. We focus on 79 members of the Noordin Top terrorist network, who were monitored attending 45 events. The attendance or nonattendance of the terrorist to events defines the social fabric, such as group coherence and social communities. The aim of the analysis of such data is to learn about the affiliation structure. Actor–event data is often transformed to actor–actor data in order to be further analysed by network models, such as stochastic block models. This transformation and such analyses lead to a natural loss of information, particularly when one is interested in identifying, possibly overlapping, subgroups or communities of actors on the basis of their attendances to events. In this paper we propose an actor–event model for overlapping communities of terrorists which simplifies interpretation of the network. We propose a mixture model with overlapping clusters for the analysis of the binary actor–event network data, called $\mathtt{manet}$, and develop a Bayesian procedure for inference. After a simulation study, we show how this analysis of the terrorist network has clear interpretative advantages over the more traditional approaches of affiliation network analysis.

中文翻译：

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从Noordin顶级演员事件网络中识别重叠的恐怖分子牢房

演员-事件数据在社会学环境中很常见，因此可以记录一组社会演员参加许多事件的方式。我们关注Noordin Top恐怖网络的79名成员，他们受到监视参加了45次活动。恐怖分子的参加或不参加事件定义了社会结构，例如团体凝聚力和社会社区。分析此类数据的目的是了解隶属结构。参与者-事件数据通常会转换为参与者-演员数据，以便通过网络模型（例如，随机块模型）进行进一步分析。这种转变和这种分析导致信息的自然流失，尤其是当人们有兴趣根据参与者对事件的参与来确定参与者的子群体或社区时，可能会重叠。在本文中，我们提出了一个针对恐怖分子重叠社区的行为者-事件模型，该模型简化了网络的解释。我们提出了一个具有重叠簇的混合模型，用于分析二元参与者-事件网络数据，称为$ \ mathtt {manet} $，并开发了一种贝叶斯推理程序。经过模拟研究，我们显示了这种对恐怖分子网络的分析与传统的隶属网络分析方法相比，具有明显的解释优势。