Identification of highly susceptible individuals in complex networks

TL;DR

This paper investigates how network structure and spreading mechanisms influence the identification of highly susceptible individuals in epidemic and rumor spreading models, revealing the importance of topological features and dynamics.

Contribution

It demonstrates that different topological indicators are optimal for identifying susceptible individuals depending on the spreading process, highlighting the role of network structure and dynamics.

Findings

k-shell outperforms degree in SIR model

Degree centrality is most effective in rumor spreading

Community structure influences overall susceptibility

Abstract

Identifying highly susceptible individuals in spreading processes is of great significance in controlling outbreaks. In this paper, we explore the susceptibility of people in susceptible-infectious-recovered (SIR) and rumor spreading dynamics. We first study the impact of community structure on people's susceptibility. Despite that the community structure can reduce the infected population given same infection rates, it will not deterministically affect nodes' susceptibility. We find the susceptibility of individuals is sensitive to the choice of spreading dynamics. For SIR spreading, since the susceptibility is highly correlated to nodes' influence, the topological indicator k-shell can better identify highly susceptible individuals, outperforming degree, betweenness centrality and PageRank. In contrast, in rumor spreading model, where nodes' susceptibility and influence have no clear correlation, degree performs the best among considered topological measures. Our finding highlights the significance of both topological features and spreading mechanisms in identifying highly susceptible population.