The biased net paradigm was the first general and empirically tractable scheme for parameterizing complex patterns of dependence in networks, expressing deviations from uniform random graph structure in terms of latent ``bias events,'' whose realizations enhance reciprocity, transitivity, or other structural features. Subsequent developments have introduced local specifications of biased nets, which reduce the need for approximations required in early specifications based on tracing processes. Here, we show that while one such specification leads to inconsistencies, a closely related Markovian specification both evades these difficulties and can be extended to incorporate new types of effects. We introduce the notion of inhibitory bias events, with satiation as an example, which are useful for avoiding degeneracies that can arise from closure bias terms. Although our approach does not lead to a computable likelihood, we provide a strategy for approximate Bayesian inference using random forest prevision. We demonstrate our approach on a network of friendship ties among college students, recapitulating a relationship between the sibling bias and tie strength posited in earlier work by Fararo.

翻译：偏置网络范式是首个通用且经验上可处理的方案，用于参数化网络中复杂的依赖模式，其通过潜在“偏置事件”的实现来表达对均匀随机图结构的偏离，这些事件能增强互惠性、传递性或其他结构特征。后续研究引入了偏置网络的局部规范，这减少了早期基于追踪过程的规范所需的近似处理。本文表明，尽管其中一种规范会导致不一致性，但一个密切相关的马尔可夫规范不仅能规避这些困难，还可扩展以纳入新型效应。我们引入了抑制性偏置事件的概念（以饱和效应为例），这有助于避免由闭合偏置项可能引发的退化问题。虽然我们的方法未能导出可计算的似然函数，但我们提出了一种利用随机森林预见的近似贝叶斯推断策略。我们在一个大学生友谊纽带网络中验证了我们的方法，重现了Fararo早期研究中提出的同胞偏置与纽带强度之间的关系。