Abstract
An alternate model for rumor spreading over small-world networks is suggested, of which two rumors (termed rumor 1 and rumor 2) have different nodes and probabilities of acceptance. The propagation is not symmetric in the sense that when deciding which rumor to adopt, high-degree nodes always consider rumor 1 first, and low-degree nodes always consider rumor 2 first. The model is a natural generalization of the well-known epidemic SIS model and reduces to it when some of the parameters of this model are zero. We find that rumor 1 (preferred by high-degree nodes) is dominant in the network when the degree of nodes is high enough and/or when the network contains large clustered groups of nodes, expelling rumor 2. However, numerical simulations on synthetic networks show that it is possible for rumor 2 to occupy a nonzero fraction of the nodes in many cases as well. Specifically, in the NW small-world model a moderate level of clustering supports its adoption, while increasing randomness reduces it.
Highlights
Rumor spreading is a complex socio-psychological process
In this paper we propose a model of rumor spreading, where two different types of rumor affect the nodes, and consider the behavior of the model on a small-world network
In Behavior of the Model on Small-World Network we describe the behavior of the model on NW small-world topology and confirm, via further analysis and dissemination simulations, a number of our calculations
Summary
Rumor spreading is a complex socio-psychological process Pioneering contributions to their modeling, based on epidemiological models, date back to (Landahl, 1953; Rapoport, 1952). In this paper we propose a model of rumor spreading, where two different types of rumor affect the nodes, and consider the behavior of the model on a small-world network. Our model follows this idea of the SIS epidemiological model. The source of rumor 1 and rumor 2 both have special meaning, and some nodes will always consider one of them first This formulation could be significant for describing four w-o-m processes circulating in a social network. We offer some concluding remarks and points out potential research directions in Conclusions and Discussion
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