# Discrete Temporal Models of Social Networks

## TL;DR

This paper introduces a new family of statistical models called temporal ERGMs for analyzing the evolution of social networks over time, extending existing ERGMs with methods for hypothesis testing and classification.

## Contribution

The paper develops a novel framework for modeling dynamic social networks using temporal ERGMs, adapting existing ERGM methods for time-evolving data.

## Key findings

- Models effectively capture social network dynamics
- Applicable to gene regulation and communication networks
- Demonstrated usefulness in hypothesis testing

## Abstract

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.