The exponential random graph (ERGM) model is a popular statistical framework for studying the determinants of tie formations in social network data. To test scientific theories under the ERGM framework, statistical inferential techniques are generally used based on traditional significance testing using p values. This methodology has certain limitations however such as its inconsistent behavior when the null hypothesis is true, its inability to quantify evidence in favor of a null hypothesis, and its inability to test multiple hypotheses with competing equality and/or order constraints on the parameters of interest in a direct manner. To tackle these shortcomings, this paper presents Bayes factors and posterior probabilities for testing scientific expectations under a Bayesian framework. The methodology is implemented in the R package 'BFpack'. The applicability of the methodology is illustrated using empirical collaboration networks and policy networks.

翻译：指数随机图(ERGM)模型是研究社交网络数据中连接关系形成决定因素的常用统计框架。在ERGM框架下检验科学理论时，通常采用基于p值的传统显著性检验统计推断技术。然而，该方法存在若干局限性，例如当原假设成立时其行为不一致，无法量化支持原假设的证据，以及无法直接对感兴趣参数同时检验包含平等约束和/或顺序约束的多个假设。为解决这些缺陷，本文提出了贝叶斯框架下检验科学期望的贝叶斯因子和后验概率。该方法已在R包'BFpack'中实现，并通过实证合作网络和政策网络案例验证了其适用性。