In statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition covariates are observed and the goal is to model the impact of the covariates on the interactions. We distinguish two types of covariates: global, system-wide covariates (i.e. covariates taking the same value for all individuals, such as seasonality) and local, dyadic covariates modeling interactions between two individuals in the network. Existing continuous time network models are extended to allow for comparing a completely parametric model and a model that is parametric only in the local covariates but has a global non-parametric time component. This allows, for instance, to test whether global time dynamics can be explained by simple global covariates like weather, seasonality etc. The procedure is applied to a bike-sharing network by using weather and weekdays as global covariates and distances between the bike stations as local covariates.

翻译：在统计网络分析中，常观测到所谓的交互数据。此类数据的特征在于，参与者构成网络的顶点，并沿着网络边进行交互，而边在观测期内随机形成和消失。此外，还观测到协变量，目标是建模协变量对交互的影响。我们区分两类协变量：全局性、系统范围的协变量（即对所有个体取值相同的协变量，如季节性）和局部性、二元的协变量，用于建模网络中两个个体之间的交互。现有连续时间网络模型得到扩展，以允许比较完全参数化模型和仅在局部协变量上参数化但具有全局非参数时间分量的模型。例如，这可检验全局时间动态是否可由简单的全局协变量（如天气、季节性等）解释。该过程应用于一个共享单车网络，使用天气和星期几作为全局协变量，自行车站点之间的距离作为局部协变量。