Faster methods for random sampling
Communications of the ACM
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
The Mathematics of Infectious Diseases
SIAM Review
Introduction to algorithms
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Proceedings of the 23rd international conference on Supercomputing
Effective vaccination policies
Information Sciences: an International Journal
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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.