The epidemic spreading on arbitrary complex networks is studied in SIR(Susceptible Infected Recovered) compartment model. We propose ourimplementation of a Naive SIR algorithm for epidemic simulation spreading onnetworks that uses data structures efficiently to reduce running time. TheNaive SIR algorithm models full epidemic dynamics and can be easily upgraded toparallel version. We also propose novel algorithm for epidemic simulationspreading on networks called the FastSIR algorithm that has better average caserunning time than the Naive SIR algorithm. The FastSIR algorithm uses novelapproach to reduce average case running time by constant factor by usingprobability distributions of the number of infected nodes. Moreover, theFastSIR algorithm does not follow epidemic dynamics in time, but still capturesall infection transfers. Furthermore, we also propose an efficient recursivemethod for calculating probability distributions of the number of infectednodes. Average case running time of both algorithms has also been derived andexperimental analysis was made on five different empirical complex networks.
展开▼
