Impact of degree truncation on the spread of a contagious process on networks

TL;DR

This study investigates how degree truncation in social networks, caused by limited data collection methods, affects the accuracy of predicting the spread of contagious processes, revealing slower and smaller epidemics with reduced prediction accuracy.

Contribution

The paper demonstrates the impact of degree truncation on epidemic spread predictions using synthetic and empirical networks, highlighting the importance of complete network data.

Findings

Truncated networks lead to slower, smaller epidemics.

Prediction accuracy drops sharply at certain truncation levels.

Impact varies depending on network properties.

Abstract

Understanding how person-to-person contagious processes spread through a population requires accurate information on connections between population members. However, such connectivity data, when collected via interview, is often incomplete due to partial recall, respondent fatigue or study design, e.g., fixed choice designs (FCD) truncate out-degree by limiting the number of contacts each respondent can report. Past research has shown how FCD truncation affects network properties, but its implications for predicted speed and size of spreading processes remain largely unexplored. To study the impact of degree truncation on spreading processes, we generated collections of synthetic networks containing specific properties (degree distribution, degree-assortativity, clustering), and also used empirical social network data from 75 villages in Karnataka, India. We simulated FCD using various truncation thresholds and ran a susceptible-infectious-recovered (SIR) process on each network. We found that spreading processes propagated on truncated networks resulted in slower and smaller epidemics, with a sudden decrease in prediction accuracy at a level of truncation that varied by network type. Our results have implications beyond FCD to truncation due to any limited sampling from a larger network. We conclude that knowledge of network structure is important for understanding the accuracy of predictions of process spread on degree truncated networks.