Dynamical analysis of rumor spreading model in homogeneous complex networks

2019 ◽  
Vol 359 ◽  
pp. 374-385 ◽  
Author(s):  
Jiarong Li ◽  
Haijun Jiang ◽  
Zhiyong Yu ◽  
Cheng Hu
2020 ◽  
Vol 536 ◽  
pp. 391-408 ◽  
Author(s):  
Jiarong Li ◽  
Haijun Jiang ◽  
Xuehui Mei ◽  
Cheng Hu ◽  
Guoliang Zhang

2019 ◽  
Vol 10 (03) ◽  
pp. 75-86
Author(s):  
Yujiang Liu ◽  
Chunmei Zeng ◽  
Youquan Luo

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 152493-152500 ◽  
Author(s):  
Liang'An Huo ◽  
Fan Ding ◽  
Tingting Lin ◽  
Shengwei Yao

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Liang’an Huo ◽  
Fan Ding ◽  
Chen Liu ◽  
Yingying Cheng

The dynamic models are proposed to investigate the influence node activity has on rumor spreading process in both homogeneous and heterogeneous networks. Different from previous studies, we believe that the activity of nodes in complex networks affects the process of rumor spreading. An active node can have contact with all the nodes it directly links to, while an inactive node could only interact with its active neighbors. We explore the joint effort of activity rate, spreading rate and network topology on rumor spreading process by mean-field equations and numerical simulations, which reveals that there exists a critical curve consisting of critical activity rate and spreading rate; meanwhile, activity rate and spreading rate both have influence on the final rumor spreading scale.


2019 ◽  
Vol 30 (06) ◽  
pp. 1950046
Author(s):  
Liang’an Huo ◽  
Tingting Lin ◽  
Chen Liu ◽  
Xing Fang

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.


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