Generalized Poisson Dynamic Network Models

TL;DR

This paper introduces generalized Poisson-based dynamic network models to better capture overdispersion in count-weighted temporal networks, improving fit and predictive performance.

Contribution

It proposes new dynamic network models using the Generalized Poisson distribution, along with Bayesian inference methods, to address overdispersion in temporal networks.

Findings

Models effectively capture overdispersion in real datasets.

Explicit modeling of overdispersion improves in-sample fit.

Neglecting overdispersion leads to bias in estimates.

Abstract

Count-weighted temporal networks often exhibit unequal dispersion in the edge weights, which cannot be fully explained by modelling observational heterogeneity through latent factors in the conditional mean. Therefore, we propose new dynamic network model classes exploiting the Generalized Poisson distribution to capture both under- and overdispersion. We consider three different dynamic specifications: latent factor dynamics, autoregressive dynamics, and latent position dynamics, and study some theoretical properties of the random networks, showing the impact of the dispersion parameter on the random network's connectivity. After discussing the parameter identification strategy, we present a Bayesian inference procedure along with a posterior sampling algorithm. A numerical illustration demonstrates the effectiveness of the designed algorithm and provides estimates of the misspecification bias when unequal dispersion is neglected. Our new models are then applied to two relevant dynamic datasets considered in previous studies: a set of bike-sharing dynamic networks and a set of dynamic media networks. Our results highlight the importance of explicitly modeling overdispersion for both an accurate in-sample fit and out-of-sample performance.