The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.

中文翻译：

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不再离散：社交网络和持续行为的动态建模

个体行为的动态与个体所嵌入的社会结构的动态相关。这意味着为了研究社会选择或同伴影响等社会机制，我们需要将社会网络的演变和网络参与者的属性建模为相互依赖的过程。随机演员导向模型是一种基于纵向数据研究网络-属性协同进化的统计方法。在其标准规范中，假设共同进化的参与者属性是在有序分类尺度上测量的。连续变量首先需要离散化以适应这样的建模框架。本文介绍了随机演员导向模型的扩展，该模型通过使用随机微分方程对连续属性的演化进行建模来消除此限制。我们提出了解释方差的度量并给出了参数大小的解释。通过对青少年友谊、饮酒和自尊之间关系的研究来说明所提出的方法。