Research on CSER Rumor Spreading Model in Online Social Network

2017 ◽  
pp. 767-775 ◽  
Author(s):  
An-Hui Li ◽  
Jing Wang ◽  
Ya-Qi Wang
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Rumor Spreading ◽  
Spreading Model

How the node’s vital and tie strength effect rumor spreading on social network

2019 ◽  
Vol 30 (06) ◽  
pp. 1950046
Author(s):  
Liang’an Huo ◽  
Tingting Lin ◽  
Chen Liu ◽  
Xing Fang
Keyword(s):  
Social Network ◽  
Complex Networks ◽  
Numerical Solutions ◽  
Spreading Rate ◽  
Tuning Parameter ◽  
Tie Strength ◽  
Time Step ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Positive Quantity

The spread of rumors on complex networks has attracted wide attention in the field of management. In this paper, the generalized rumor spreading model is modified to take into account the vital of the spreader and the tie strength for the pairwise contacts between nodes in complex networks at degree-dependent spreading rate. Concretely, we introduce the infectivity exponent [Formula: see text], and the degree influenced real exponent [Formula: see text] into the analytical rumor spreading model. Rumor infectivity, [Formula: see text], where [Formula: see text], defines that each spreader node may contact [Formula: see text] neighbors within one time step. The tie strength between two nodes with degrees [Formula: see text] and [Formula: see text] are measured by [Formula: see text], [Formula: see text] is the degree influenced real exponent which depends on the type of complex networks and [Formula: see text] is a positive quantity. We use a tuning parameter [Formula: see text] to combine both the effect of the vital nodes and the strength of connectivity between nodes. We use analytical and numerical solutions to examine the threshold behavior and dynamics of the model on several models of social network. It was found that the infectivity exponent [Formula: see text], the degree influenced real exponent [Formula: see text] and tuning parameter [Formula: see text] affect the rumor threshold, one can adjust the parameters to control the rumor threshold which is absent for the standard rumor spreading model.

Research on twin-SIR rumor spreading model in online social network

2020 ◽  
pp. 1-12
Author(s):  
Jing Yi ◽  
Peiyu Liu ◽  
Zhihao Wang ◽  
Wenfeng Liu
Keyword(s):  
Online Social Network ◽  
Mean Field ◽  
Traditional Media ◽  
Spreading Process ◽  
Rumor Spreading ◽  
News Reports ◽  
Spreading Model ◽  
The Mean ◽  
Mean Field Equation ◽  
Selection Of

In the study filed of rumor spreading, kill rumor or dispel rumor is very important in order to control rumor spreading and reduce the bad influence of the rumor. In the previous studies, rumor clarification is mostly finished by relying on external media or news reports instead of intervening and controlling from inside the network, which causes that the speed of rumor clarification is far lower than the speed of rumor spreading, and it is not ideal for the effect of rumor clarification. In this paper, a new Twin-SIR spreading model is proposed, in which, a rumor clarification node named as “rumor dispeller” with the spreading ability is introduced. The rumor dispeller is involved in the spreading process of the model together with the rumor spreader to control the spreading of rumor and thus to achieve the purpose of clarifying rumor. At the same time, during the process of building the model, we also apply the traditional media as a spreading parameter to the spreading process of the model. We built the mean-field equation of the model and then implemented further analysis of the model on homogeneous networks and heterogeneous networks. Through experimental simulations, the “rumor dispeller” was found to have the ability to reduce the spread of rumor spreading, and that the selection of the initial “rumor dispeller” node can affect the effect of rumor spreading, and at the same time, the external media have an important influence on rumor clarification. These conclusions have a new function for guiding us to study the mechanism of rumor spreading.

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