On inferring rumor source for SIS model under multiple observations

This paper studies the problem of a single rumor source detection based on the susceptible-infected-susceptible (SIS) spreading model. Based on the rumor centrality proposed in the Susceptible-Infected (SI) model by Shah and Zaman, we propose a rumor centrality based algorithm, that leverages multiple observations to first construct a diffusion tree graph, and then use the union rumor centrality to find the rumor source. Our simulation results on different network structures shows that our proposed algorithm performs well. For tree networks, increasing the observations can dramatically improve the exact detection probability. This clearly indicates that a richer diversity enhances detect-ability.