Whalen Andrew, Hoppitt William J E
School of Biology, University of St. Andrews St. Andrews, UK.
School of Biology, University of Leeds Leeds, UK.
Front Psychol. 2016 Apr 5;7:409. doi: 10.3389/fpsyg.2016.00409. eCollection 2016.
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.
最近的一些研究使用基于网络的扩散分析（NBDA）来检测社会传播在新行为在人群中传播中的作用。在本文中，我们提出了一个在贝叶斯环境中执行NBDA的统一框架，并演示了如何将渡边赤池信息准则（WAIC）用于模型选择。我们给出了将此方法应用于获取时间扩散分析（TADA）的具体示例。为了检验该技术的稳健性，我们进行了大规模模拟研究，发现使用WAIC的NBDA在广泛的情况下，包括存在随机效应、个体水平变量和社会传播替代模型的情况下，都能恢复正确的社会传播模型。这项工作表明，NBDA是一种有效且广泛适用的工具，可用于揭示社会传播是否是新行为传播的基础，并且即使关键模型假设放宽，仍可能提供准确的结果。
