Efficient Estimation of Cumulative Influence for Multiple Activation Information Diffusion Model with Continuous Time Delay

We show that the node cumulative influence for a particular class of information diffusion model in which a node can be activated multiple times, i.e. Susceptible/Infective/Susceptible (SIS) Model, can be very efficiently estimated in case of independent cascade (IC) framework with asynchronous time delay. The method exploits the property of continuous time delay within a stochastic framework and analytically derives the iterative formula to estimate cumulative influence without relying on awfully lengthy simulations. We show that it can accurately estimate the cumulative influence with much less computation time (about 2 to 6 orders of magnitude less) than the naive simulation using three real world social networks and thus it can be used to rank influential nodes quite effectively. Further, we show that the SIS model with a discrete time step, i.e. fixed synchronous time delay, gives adequate results only for a small time span.

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