The conventional exponential family random graph model (ERGM) parameterization leads to a baseline density that is constant in graph order (i.e., number of nodes); this is potentially problematic when modeling multiple networks of varying order. Prior work has suggested a simple alternative that results in constant expected mean degree. Here, we extend this approach by suggesting another alternative parameterization that allows for flexible modeling of scenarios in which baseline expected degree scales as an arbitrary power of order. This parameterization is easily implemented by the inclusion of an edge count/log order statistic along with the traditional edge count statistic in the model specification.

中文翻译：

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多网络 ERGM 中基线平均度的灵活参数化

传统的指数族随机图模型（ERGM）参数化导致基线密度在图顺序（即节点数）上是恒定的；在对不同顺序的多个网络进行建模时，这可能会出现问题。先前的工作提出了一个简单的替代方案，可以产生恒定的预期平均程度。在这里，我们通过提出另一种替代参数化来扩展这种方法，该参数化允许对基线预期程度缩放为任意阶次幂的场景进行灵活建模。通过在模型规范中包含边计数/日志顺序统计以及传统的边计数统计，可以轻松实现这种参数化。