来自响应者驱动抽样的聚类网络树数据及其在纽约市阿片类药物使用者中的应用
Clustering Network Tree Data From Respondent-driven sampling with application to opioid users in New York City
摘要：
有很大的兴趣，找到有意义的子组的属性网络数据。有许多可用的方法来聚类完整的网络。不幸的是，许多网络数据是通过采样收集的，因此是不完整的。响应者驱动抽样（RDS）是一种广泛使用的方法，用于对难以接触的人群进行抽样，该方法基于跟踪潜在未观察到的社交网络中的链接。因此，所得到的数据具有表示网络的子样本的树结构以及许多节点属性。在本文中，我们介绍了一种方法来调整混合模型的一般网络聚类采样RDS。我们将我们的模型应用于纽约市阿片类药物使用者的数据，并检测反映干预活动感兴趣的群体特征的社区，包括药物使用模式，社会联系和其他社区变量
英文摘要：
There is great interest in finding meaningful subgroups of attributed network data. There are many available methods for clustering complete network. Unfortunately, much network data is collected through sampling, and therefore incomplete. Respondent-driven sampling (RDS) is a widely used method for sampling hard-to-reach human populations based on tracing links in the underlying unobserved social network. The resulting data therefore have tree structure representing a sub-sample of the network, along with many nodal attributes. In this paper, we introduce an approach to adjust mixture models for general network clustering for samplings by RDS. We apply our model to data on opioid users in New York City, and detect communities reflecting group characteristics of interest for intervention activities, including drug use patterns, social connections and other community variables
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
