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Extracting influential nodes for information diffusion on a social network
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Minimizing the spread of contamination by blocking links in a network
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Efficient estimation of influence functions for SIS model on social networks
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Discovering Influential Nodes for SIS Models in Social Networks
DS '09 Proceedings of the 12th International Conference on Discovery Science
Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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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.