Identification of highly susceptible individuals in complex networks

• We investigate the susceptibility of individuals in SIR and rumor spreading models.
• We explore the impact of community structure on spreading processes.
• We compare the performance of degree, betweenness centrality, PageRank and k-shell in locating highly susceptible individuals.
• We find that k-shell is superior in identifying high risk population for SIR model, but degree performs better for rumor spreading model.
• Our finding highlights the significance of both topological features and spreading mechanisms in identifying highly susceptible population.

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. Although the community structure can reduce the number of infected people for same infection rate, it will not significantly 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 kk-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.