# Probing empirical contact networks by simulation of spreading dynamics

## TL;DR

This paper reviews recent research on simulating spreading processes like diseases on empirical contact networks, emphasizing temporal proximity data and analyzing 29 networks to understand how network structure influences spreading dynamics.

## Contribution

It provides a comprehensive summary of methods and findings from recent studies using simulation on empirical contact networks, especially temporal proximity networks.

## Key findings

- Insights into how network structure affects spreading dynamics
- Analysis of 29 empirical contact networks
- Focus on disease spread in temporal proximity networks

## Abstract

Disease, opinions, ideas, gossip, etc. all spread on social networks. How these networks are connected (the network structure) influences the dynamics of the spreading processes. By investigating these relationships one gains understanding both of the spreading itself and the structure and function of the contact network. In this chapter, we will summarize the recent literature using simulation of spreading processes on top of empirical contact data. We will mostly focus on disease simulations on temporal proximity networks -- networks recording who is close to whom, at what time -- but also cover other types of networks and spreading processes. We analyze 29 empirical networks to illustrate the methods.