Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics

Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of ho...背景：网络中相互连接的个体（或节点）相较于随机选取的两个节点，更易呈现相似性，该现象源于同质性（homophily）与/或网络影响。区分这两类影响机制是网络分析领域的重要研究目标，而针对纵向二元网络数据开展的广义估计方程（generalized estimating equation, GEE）分析则是颇具吸引力的研究路径。目前尚不明确这类回归模型能够在多大程度上精准还原其底层数据生成过程。因此本研究的核心目标为：探究GEE方法可在何种程度、何种条件下，复现基于智能体模型（agent-based model）中的真实动态演化过程。 方法：我们针对静态与动态网络生成了具备预设网络特征与连接关系的模拟队列，并对同质性与网络影响的存在情况进行变量调控。随后我们采用统计回归方法，在每个队列中检验GEE模型的性能，以判断该模型能否检测到ho...的存在。