Homophily and social influence are two key concepts of social network analysis. Distinguishing between these phenomena is difficult, and approaches to disambiguate the two have been primarily limited to longitudinal data analyses. In this study, we provide sufficient conditions for valid estimation of social influence through cross-sectional data, leading to a novel homophily-adjusted social influence model which addresses the backdoor pathway of latent homophilic features. The oft-used network autocorrelation model (NAM) is the special case of our proposed model with no latent homophily, suggesting that the NAM is only valid when all homophilic attributes are observed. We conducted an extensive simulation study to evaluate the performance of our proposed homophily-adjusted model, comparing its results with those from the conventional NAM. Our findings shed light on the nuanced dynamics of social networks, presenting a valuable tool for researchers seeking to estimate the effects of social influence while accounting for homophily. Code to implement our approach is available at https://github.com/hanhtdpham/hanam.

翻译：同质性与社会影响是社会网络分析中的两个核心概念。区分这两种现象较为困难，现有方法主要局限于纵向数据分析来消除二者的歧义。本研究提出了通过横截面数据有效估计社会影响的充分条件，进而提出了一种新颖的同质性调整社会影响模型，该模型解决了潜在同质性特征的后门路径问题。常用的网络自相关模型（NAM）是我们所提出模型在无潜在同质性情况下的特例，这表明NAM仅在所有同质性属性均可观测时才有效。我们进行了广泛的模拟研究以评估所提出的同质性调整模型的性能，并将其结果与传统NAM的结果进行比较。我们的研究结果揭示了社会网络的微妙动态，为研究者提供了一种在考虑同质性的同时估计社会影响效应的宝贵工具。实现我们方法的代码可在 https://github.com/hanhtdpham/hanam 获取。