Rumor Propagation Model for Complex Network with Non-Uniform Propagation Rates

Considering propagation characteristics and affecting factors of rumor in real-world complex networks, this paper described different propagation rates of different nodes by introducing the rumor acceptability function. Based on mean-field theory, this paper presented a rumor propagation model with non-uniform propagation rate, and then simulated the behaviour of rumor propagation on scale-free network and calculated the propagation thresholds by corresponding dynamics equation. Theoretical analysis and simulation results show that nodes with different rumor acceptability could lead to slowing the spread of rumors, make positive propagation threshold arise, and effectively contain the outbreak and reduce the risk of rumors.

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Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

Complexity ◽

10.1155/2020/5642730 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Kai Xu ◽

Jianming Mo ◽

Qian Qian ◽

Fengying Zhang ◽

Xiaofeng Xie ◽

...

Keyword(s):

Field Theory ◽

Credit Risk ◽

Mean Field Theory ◽

Mean Field ◽

Stable State ◽

Scale Free Network ◽

Scale Free ◽

Free Network ◽

The Mean ◽

Credit Risk Contagion

Associated credit risk is a kind of credit risk among the associated credit entities formed by credit-related entities. Focusing on this hot topic of associated credit risk and the relevant contagion and considering the latent entities and their incubatory period, this paper builds an infectious dynamic model to describe the associated credit risk contagion of associated credit entities based on the mean-field theory of complex networks. Firstly, this paper analyzes the stable state of the associated credit risk contagion in the associated entity network, considering the latent entities and their incubatory period. Secondly, from the perspective of complex network and considering the incubatory period, a SHIS model is built to reveal how the incubatory period influences associated credit risk contagion. Finally, the sensitivity of some parameters is analyzed in the Barabási–Albert (BA) scale-free network. The results show the following: (i) the contagion threshold of associated credit risk is related to the incubatory period of latent entities, the recovery rate and infectivity of infected entities, and the newborn rate of credit entities; (ii) the infectious rate of infected entities, the mortality rate of credit entities, and the important factors stated in (i) are all significantly correlated with the density of infected entities.

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Agent-Based Simulation of Rumor Propagation on Social Network Based on Active Immune Mechanism

Journal of Systems Science and Information ◽

10.21078/jssi-2017-571-14 ◽

2017 ◽

Vol 5 (6) ◽

pp. 571-584 ◽

Cited By ~ 2

Author(s):

Jianhong Chen ◽

Qinghua Song ◽

Zhiyong Zhou

Keyword(s):

Social Network ◽

Online Social Network ◽

Propagation Model ◽

Immune Mechanism ◽

Scale Free Network ◽

Propagation Process ◽

Agent Based ◽

Scale Free ◽

Rumor Propagation ◽

Free Network

AbstractTo simulate the rumor propagation process on online social network during emergency, a new rumor propagation model was built based on active immune mechanism. The rumor propagation mechanisms were analyzed and corresponding parameters were defined. BA scale free network and NW small world network that can be used for representing the online social network structure were constructed and their characteristics were compared. Agent-based simulations were conducted on both networks and results show that BA scale free network is more conductive to spreading rumors and it can facilitate the rumor refutation process at the same time. Rumors paid attention to by more people is likely to spread quicker and broader but for which the rumor refutation process will be more effective. The model provides a useful tool for understanding and predicting the rumor propagation process on online social network during emergency, providing useful instructions for rumor propagation intervention.

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Evolution of node impact based on secondary propagation

Modern Physics Letters B ◽

10.1142/s021798492050027x ◽

2019 ◽

Vol 34 (02) ◽

pp. 2050027

Author(s):

Fuzhong Nian ◽

Kai Gao

Keyword(s):

Real Life ◽

Propagation Model ◽

Information Propagation ◽

Actual Situation ◽

Scale Free Network ◽

Scale Free ◽

Initial Stage ◽

Free Network ◽

Three Stages ◽

Stable Stage

In real life, the propagation ability of the information disseminator is one of the important factors which is determined to propagate information. The influence of the node, which is altered with time, is proposed to reflect the propagation ability of the information disseminator for the significance of the information propagation in the actual situation in this paper. Therefore, the influence of the node is divided into the high-impact node and the low-impact node. Furthermore, the SSIR information propagation model is proposed and the dynamic BA scale-free network is constructed to carry out evolution of node impact based on secondary propagation experiments. The experiment results indicate three stages, including the initial stage, the rapidly rising stage and the stable stage. The propagation details of the different messages are distinct. However, the trend of propagation is similar.

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Analysis of epidemic propagation using mean field theory on signed graphs

Modern Physics Letters B ◽

10.1142/s0217984921505941 ◽

2022 ◽

Author(s):

Hongwei Su ◽

Zi-Wei Zhang ◽

Guoxing Wen ◽

Guan Yan

Keyword(s):

Field Theory ◽

Infection Rate ◽

Random Network ◽

Mean Field Theory ◽

Mean Field ◽

Propagation Model ◽

Sis Model ◽

Signed Networks ◽

Scale Free ◽

Negative Links

Over the past few decades, the study of epidemic propagation has caught widespread attention from many areas. The field of graphs contains a wide body of research, yet only a few studies explore epidemic propagation’s dynamics in “signed” networks. Motivated by this problem, in this paper we propose a new epidemic propagation model for signed networks, denoted as S-SIS. To explain our analysis, we utilized the mean field theory to demonstrate the theoretical results. When we compare epidemic propagation through negative links to those only having positive links, we find that a higher proportion of infected nodes actually spreads at a relatively small infection rate. It is also found that when the infection rate is higher than a certain value, the overall spreading in a signed network begins showing signs of suppression. Finally, in order to verify our findings, we apply the S-SIS model on Erdös–Rényi random network and scale-free network, and the simulation results is well consist with the theoretical analysis.

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Dynamic Analysis and Optimal Control of ISCR Rumor Propagation Model with Nonlinear Incidence and Time Delay on Complex Networks

Discrete Dynamics in Nature and Society ◽

10.1155/2021/3935750 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Zhongxue Chang ◽

Haijun Jiang ◽

Shuzhen Yu ◽

Shanshan Chen

Keyword(s):

Optimal Control ◽

Time Delay ◽

Complex Networks ◽

Minimum Principle ◽

Mean Field Theory ◽

Mean Field ◽

Propagation Model ◽

Nonlinear Incidence ◽

Rumor Propagation ◽

Spreading Dynamics

An Innocents-Spreaders-Calmness-Removes (ISCR) rumor propagation model is established with nonlinear incidence and time delay on complex networks in this paper. Based on the mean-field theory, the spreading dynamics of the ISCR model are discussed in detail. Firstly, the basic reproduction number R 0 is obtained by the next generation matrix method to ensure the existence of rumor-prevailing equilibrium. Secondly, by utilizing the Routh–Hurwitz criterion and LaSalle’s invariance principle, the local stability and global stability of rumor equilibria are proved. Moreover, the optimal control is presented via Pontryagin’s minimum principle, which is to effectively restrain rumor diffusion. Finally, the theoretical results are verified by numerical simulations.

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Rumor-Propagation Model with Consideration of Refutation Mechanism in Homogeneous Social Networks

Discrete Dynamics in Nature and Society ◽

10.1155/2014/659273 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Laijun Zhao ◽

Xiaoli Wang ◽

Jiajia Wang ◽

Xiaoyan Qiu ◽

Wanlin Xie

Keyword(s):

Social Networks ◽

Mean Field ◽

Propagation Rate ◽

Spreading Rate ◽

Propagation Model ◽

Critical Threshold ◽

Field Equations ◽

Rumor Propagation ◽

Mean Field Equations ◽

The Stability

In recent years, increasing attention has been paid to how to effectively manage rumor propagation. Based on previous studies of rumor propagation and some strategies used by the authorities to refute rumors and manage rumor propagation, we develop a new rumor-propagation model with consideration of refutation mechanism. In this paper, we describe the dynamic process of rumor propagation by accounting for the refutation mechanism in homogeneous social networks. And then, we derive mean-field equations for rumor-propagation process. We then analyze the stability of the model with respect to changes in parameter values. Our results show that there exists a critical thresholdλcthat is inversely proportional to the average degree of the social networks and is positively correlated with the strength of the refutation mechanism. If the spreading rate is bigger than the critical thresholdλc, rumors can be spread. Our numerical simulations in homogeneous networks demonstrate that increasing the ignorant’s refutation rateβcan reduce the peak value of spreaders density, which is better than increasing the spreader’s refutation rateη. Therefore, based on the seriousness of the rumor propagation and the rumor-propagation rate, the authorities can choose effective strategies that increase the refutation rate so that they can reduce the maximum influence of the rumor.

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