الگوریتم FastSIR: یک الگوریتم سریع برای شبیه سازی گسترش اپیدمی در شبکه های بزرگ با استفاده از مدل محفظه حساس-آلوده-بهبود

We propose two efficient epidemic spreading algorithms (Naive SIR and FastSIR) for arbitrary network structures, based on the SIR (susceptible–infected–recovered) compartment model. The Naive SIR algorithm models full epidemic dynamics of the well-known SIR model and uses data structures efficiently to reduce running time. The FastSIR algorithm is based on the probability distribution over the number of infected nodes and uses the concept of generation time instead of explicit time in treating the spreading dynamics. Furthermore, we also propose an efficient recursive method for calculating probability distributions of the number of infected nodes. The average case running time of both algorithms has also been derived and an experimental analysis was made on five different empirical complex networks.