Models for cross-sectional network data have become increasingly well-developed in recent decades, and are widely used. This has led to a growing interest in the connection between such cross-sectional models and the behavioral processes from which the corresponding networks were presumably generated. Here, we build on prior work in this area to present a behavioral micro-foundation for cross-sectional network models, based on a continuous time stochastic choice mechanism, that can accommodate highly general classes of cases (including agents who are not themselves in the network, and multilateral edge control). As we show, the equilibrium behavior of this process under appropriate conditions can be expressed in exponential family form, allowing estimation of individual preferences using existing methods; the graph potential separates naturally into a preference-based term reflecting agent utilities, and an entropic term reflecting the rules of tie formation. We illustrate our approach via an analysis of friendship in a professional organization, and modeling of phase transitions in the structure of small groups.

翻译：近几十年来，横截面网络数据模型日益成熟并得到广泛应用，这促使学界愈发关注此类横截面模型与促成网络形成的行为过程之间的联系。本文在前人研究基础上，提出一种基于连续时间随机选择机制的横截面网络模型行为微观基础，该框架能够涵盖高度一般化的情形（包括不在网络中的行动者及多边边控制）。研究表明，在适当条件下该过程的均衡行为可表示为指数族形式，从而允许利用现有方法估计个体偏好；图势函数自然地分解为反映行动者效用的偏好项与反映连接形成规则的熵项。我们通过对某专业组织中的友谊关系分析以及小群体结构中的相变建模，展示了该方法的有效性。