# Score-Driven Exponential Random Graphs: A New Class of Time-Varying Parameter Models for Dynamical Networks

## TL;DR

This paper introduces score-driven ERGMs, a novel class of models that capture the time-varying nature of network parameters, improving the modeling and prediction of dynamic networks in finance and politics.

## Contribution

It extends ERGMs with score-driven dynamics, enabling flexible modeling of evolving network structures and outperforming static models in real-world applications.

## Key findings

- Enhanced prediction of future links in financial networks.
- Effective discrimination between static and dynamic network parameters.
- Demonstrated flexibility of SD-ERGMs as data-generating processes.

## Abstract

Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the Exponential Random Graph Models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on Dynamic Conditional Score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics.