# Dynamic Network Prediction

## TL;DR

This paper introduces a statistical framework for predicting dynamic social networks that accounts for uncertainty and variability, aiding in designing effective network-based interventions.

## Contribution

It presents a novel, flexible method for sampling predicted networks from evolving network properties, incorporating uncertainty unlike previous approaches.

## Key findings

- Demonstrated the method's usefulness through simulation studies.
- Applied the framework to US Senate bill sponsorship networks.
- Showed potential for improving intervention strategies.

## Abstract

We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the US Senate from 2003-2016.