A Partially Separable Model for Dynamic Valued Networks

TL;DR

This paper introduces a novel Partially Separable Temporal ERGM for modeling dynamic valued networks, enabling better interpretation and forecasting of network evolution with count data.

Contribution

It develops a new ERGM framework for dynamic valued networks with count data, incorporating temporal dependencies and advanced estimation techniques.

Findings

Successfully models network dynamics with real data

Enables interpretation of temporal network changes

Provides accurate forecasts of network evolution

Abstract

The Exponential-family Random Graph Model (ERGM) is a powerful model to fit networks with complex structures. However, for dynamic valued networks whose observations are matrices of counts that evolve over time, the development of the ERGM framework is still in its infancy. To facilitate the modeling of dyad value increment and decrement, a Partially Separable Temporal ERGM is proposed for dynamic valued networks. The parameter learning algorithms inherit state-of-the-art estimation techniques to approximate the maximum likelihood, by drawing Markov chain Monte Carlo (MCMC) samples conditioning on the valued network from the previous time step. The ability of the proposed model to interpret network dynamics and forecast temporal trends is demonstrated with real data.