Dynamics of rumor propagation on small-world networks

Abstract

We study the dynamics of an epidemic-like model for the spread of a rumor on a small-world network. It has been shown that this model exhibits a transition between regimes of localization and propagation at a finite value of the network randomness. Here, by numerical means, we perform a quantitative characterization of the evolution in the two regimes. The variant of dynamic small worlds, where the quenched disorder of small-world networks is replaced by randomly changing connections between individuals, is also analyzed in detail and compared with a mean-field approximation.

Mathematical modelling for the social impact to energy efficiency savings

“…In literature [24]- [26], it is assumed that the information passed from person A to person B is the full information and B understands the information. In real life this is not the case, there is always some information that is lost during transmission.…”

Critical behaviour of irreversible reaction systems

“…Some typical examples include the spreading of epidemics through a population, the propagation of rumors in a society [30], the spreading of fire through a forest [31,32], coexistence with extinction transitions in prey-predator systems [33] and, of course, catalytic and autocatalytic chemical reactions. From a more general point of view, absorbing states are expected to occur in situations where some quantity of interest can proliferate or die out ( e.g the fire in the forest), without any possibility of spontaneous generation (e.g due to the rays of an electrical storm).…”

“…Among others, KPZ and EW universality classes are the most frequently found in both experiments and models [67], including electrochemical deposition, polycrystalline thin-film growth, fire-front propagation, etc. [30,31,32,33].…”

“…The reactions given by equations (31) and (32) are assumed to be instantaneous (infinity reaction rate limit) while the limiting steps are the adsorption events given by equations (29) and (30 [134,135,136,137,138,139].…”

An Evolutionary Game Model of Multi-Topics Diffusion in Social Network

“…The participants in a social network can form communities [2], influence other participants [3]. One major function of social networks (in particular, massive online social networks) is the dissemination of information such as news, comments, and rumors [4][5][6][7]. As an important form of social organization, Information can shape public opinion, inform public behavior [5], further, rumors can spread astoundingly fast through social networks [8].…”