A0348
Title: Testing for global covariate effects in dynamic interaction event networks
Authors: Alexander Kreiss - Leipzig University (Germany) [presenting]
Enno Mammen - Heidelberg University (Germany)
Wolfgang Polonik - University of California at Davis (United States)
Abstract: 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 the 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. Two types of covariates are distinguished: 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 nonparametric time component. This allows, for instance, testing 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.
