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NETWORK INFERENCE FROM GROUPED OBSERVATIONS USING HUB MODELS

机译:使用集线器模型进行分组观测的网络推断

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摘要

In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been developed to infer network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. To address this issue, we propose a model-based approach called the Hub Model which assumes that every observed group has a leader and that the leader has brought together the other members of the group. The performance of Hub Models is demonstrated by simulation studies. We apply this model to the characters in a famous 18th century Chinese novel.
机译:在医学研究,经济学和社会科学数据中经常出现为一组对象的子集。 在过去的世纪中,已经开发了许多描述性统计数据来从这些数据中推断网络结构。 然而,这些措施缺乏产生的机制,该产生机制将推断的网络结构与观察到的组联系起来。 为了解决这个问题,我们提出了一种称为集线器模型的基于模型的方法,该方法假设每个观察到的集团都有一个领导者,并且领导者汇集了集团的其他成员。 通过仿真研究证明了集线器模型的性能。 我们将此模型应用于着名18世纪中国小说中的人物。

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