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Sensitivity analysis for network observations with applications to inferences of social influence effects
Network Science Pub Date : 2020-10-19 , DOI: 10.1017/nws.2020.36
Ran Xu , Kenneth A. Frank

The validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors’ preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.

中文翻译:

网络观察的敏感性分析与社会影响效应推断的应用

网络观察的有效性有时在实证研究中受到关注,因为观察到的网络容易出错并且可能不代表感兴趣的人群。这种缺乏有效性不仅是随机测量误差的结果,而且通常是由于系统偏差导致对参与者对网络选择的偏好的误解。网络观察中的这些问题可能会使对常见网络模型(例如与影响和选择有关的模型)的估计产生偏差,并导致错误的统计推断。在这项研究中,我们提出了一种基于模拟的敏感性分析方法,该方法可以评估社交网络分析中对六种可能导致网络观察偏差的选择机制的鲁棒性——随机、同质、反同质、传递性、互惠, 和优先依附。然后,我们应用这种敏感性分析来测试推论对社会影响效应的稳健性,并且我们得出了两组分析解决方案,可以解释由于随机、同质和反同质选择导致的网络观察偏差。
更新日期:2020-10-19
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