Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1781-1786. doi: 10.1109/EMBC46164.2021.9629746.
Respondent-driven sampling (RDS) is a popular method for surveying hidden populations based on friendships and existing social network connections. In such a survey the underlying hidden network remains largely unknown. However, it is useful to estimate its size as well as the relative proportions of surveyed features. The fact that linked network participants are likely to share common features is called homophily, and is an important property in understanding the topology of social networks. In this paper we present a methodology that scales up RDS data to model the underlying hidden population in a way that preserves multiple homophilies among different features. We test our model using 46 features of the population sampled by the SATHCAP RDS survey. Our network generation methodology successfully preserves the homophilic associations in a randomly generated Barabasi-Albert network. Having created a realistic model of the expanded SATHCAP network, we test our model by simulating RDS surveys over it, and comparing the resulting sub-networks with SATHCAP. In our generated network, we preserve 85% of homophilies to under 2% error. In our simulated RDS surveys we preserve 85% of homophilies to under 15% error.
响应者驱动抽样 (RDS) 是一种基于友谊和现有社交网络联系来调查隐藏人群的流行方法。在这样的调查中，底层的隐藏网络在很大程度上是未知的。然而，估计其大小以及被调查特征的相对比例是很有用的。链接网络参与者很可能具有共同特征的事实称为同质性，这是理解社交网络拓扑结构的一个重要属性。在本文中，我们提出了一种方法，该方法可以扩展 RDS 数据，以保留不同特征之间的多种同质性，从而对底层隐藏人群进行建模。我们使用 SATHCAP RDS 调查抽样的人口的 46 个特征来测试我们的模型。我们的网络生成方法成功地在随机生成的 Barabasi-Albert 网络中保留了同质性关联。在创建了扩展的 SATHCAP 网络的逼真模型之后，我们通过在其上模拟 RDS 调查来测试我们的模型，并将生成的子网与 SATHCAP 进行比较。在我们生成的网络中，我们将 85%的同质性保留在 2%的误差以下。在我们的模拟 RDS 调查中，我们将 85%的同质性保留在 15%的误差以下。
