Abstract
In this paper, incubation mechanism and activity of nodes are introduced to study the dynamics mechanism of rumor spreading in complex network. It is assumed that when the susceptible interacts with the rumor believers, the susceptible need time to think about the rumor before they believe it. The susceptible individual will first go through a incubation period after being infected before becoming believer or stifler. The nodes have active and the inactive states, it can convert each other with a certain probability, the active nodes can interact with others which directs linking, and the inactive nodes just response the active neighbors. The dynamic rumor spreading model was built by mean-field equations in heterogeneous network, and then the numerical simulations were proposed which reveals that the joint efforts of activity rate and spreading rate have negative effects on final rumor spreading size, and the simulations results also confirm the theoretical analysis in the modified model.
Highlights
Rumor is a type of gossip which lack the evidence and unconfirmed
A modified model is established to study the impact of incubation mechanism and activity of nodes on rumor spreading process
The simulation found that both spreading rate and activity rate have positive impact on the threshold of rumor spreading. when the activity rate is no smaller than its threshold, activity rate’s effect on spreading dynamics is rather obvious
Summary
Rumor is a type of gossip which lack the evidence and unconfirmed. Because of the presence of social network applications, rumor is more prone to spread online instead of word by mouth in large regions. L. Huo et al.: Dynamical Analysis of Rumor Spreading Model With Incubation Mechanism and Activity of Nodes effect of the heterogeneity of network on the threshold of rumor spreading [9], [12]. A modified model is established to study the impact of incubation mechanism and activity of nodes on rumor spreading process. TSaS (t), tSaIn (t) can be deduced as same way, they represent an active susceptible node at time t for arbitrary g hold susceptible state and transit with probabilities respectively. In the limit t → 0, we can obtain dSk (t) dt
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