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Preprints Archive: Abstract of IC2010088 (2010)
Effects of degree-biased transmission rate and nonlinear infectivity on rumor spreading in complex social networks
Document info: Pages 13, Figures 0.
We introduce the generalized rumor spreading model and analytically investigate the epidemic spreading for this model on scale-free networks. To generalize the standard rumor spreading model (rumor model in which each node's infectivity equals its degree and all links have a uniform connectivity strength), we introduce not only the infectivity function to determine the simultaneous contacts that a given node (individual) establishes to its connected neighbors but also the connectivity strength function (CSF) for the direct link between two connected nodes that lead to degree-biased propagation of rumors. In the case of nonlinear functions, the generalization enters the infectivity's exponent $\alpha$ and the CSF's exponent $\beta$ into the analytical rumor model. We show that one can adjust the exponents $\alpha$ and $\beta$ to control the epidemic threshold which is absent for the standard rumor spreading model. In addition, we obtain the critical threshold for the generalized model on the finite scale-free network and compare our results with the standard model on the same network. We show that the generalized model has a greater threshold than the standard model.