Testing For Global Covariate Effects in Dynamic Interaction Event Networks

TL;DR

This paper develops a statistical testing framework to evaluate the influence of global covariates on dynamic interaction networks, extending existing models to include non-parametric global time effects and applying it to a bike-sharing system.

Contribution

It introduces a method to test for global covariate effects in dynamic networks, allowing for non-parametric global time components and comparing them to parametric models.

Findings

Global covariates like weather significantly impact bike-sharing interactions.

The proposed test can distinguish between simple parametric and complex non-parametric global effects.

Application demonstrates the method's effectiveness in real-world network data.

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 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.