论文标题
从受访者驱动的抽样中将网络树数据与纽约市阿片类用户的应用程序进行聚类
Clustering Network Tree Data From Respondent-driven sampling with application to opioid users in New York City
论文作者
论文摘要
寻找有意义的属性网络数据子组非常感兴趣。有许多可用的方法用于聚类完整网络。不幸的是，通过采样收集了许多网络数据，因此不完整。受访者驱动的抽样（RDS）是一种基于基础未观察到的社交网络中的追踪链接来取样难以触及的人群的广泛使用方法。因此，所得数据具有代表网络子样本的树结构以及许多节点属性。在本文中，我们引入了一种方法，以调整RDS采样的一般网络聚类的混合模型。我们将模型应用于纽约市阿片类用户的数据，并检测社区反映了对干预活动感兴趣的群体特征，包括吸毒模式，社交联系和其他社区变量
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
