Data-Collection Strategies for the Application of Conditional Dynamic Network Models

• Activity Number: 79
• Type: Contributed
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Social Statistics Section
• Author(s): Zack Almquist* and Yang Yang
• Companies: University of Minnesota and University of Minnesota
• Keywords: social networks ; dynamic networks ; simulation ; exponential models ; logistic models

Abstract

The popularity of conditional dynamic models for network panel data has greatly increased over the recent decade; however, there are few practical heuristics for what level of temporal sampling -- e.g. weekly, daily or hourly snapshots -- of the evolving dynamic network is required for modeling and estimation purposes. We investigate these models through simulation so as to ascertain when these models are appropriate and when these models should be avoided. Here, we use parameters from empirical cases in the literature to inform a series of simulation based studies where we generate synthetic datasets which we then subsample at well chosen time intervals to test the model's ability to recreate the known parameters and macro-level network properties of interest. Beyond purely testing for parameter degradation we also examine information metrics and a series of predictive checks as proposed in the statistical network literature.