A Rumor Spreading Model considering the Cumulative Effects of Memory

Abstract EN

This paper proposes a rumor spreading model which examines how the memory effects rate changes over time in artificial network and a real social network.

This model emphasizes a special rumor spreading characteristic called “the cumulative effects of memory.” A function p(t) reflecting the cumulative memory effects is established, which replaces the constant rate of memory effects in the traditional model.

Further, rumor spreading model simulations are conducted with different parameters in three artificial networks.

The results show that all the parameters but the initial memory rate of memory effects function have a significant impact on rumor spreading.

At the same time, the simulation results show that the final size of the stiflers is sensitive to the average degree k when it is small but is not sensitive to k when the average degree is greater than a certain degree.

Finally, through investigations on the Sina Microblog network, the numerical solutions show that the peak value and final size of the rumor spreading are much larger under a variable memory effects rate than under a constant rate.