scholarly journals Dynamical Analysis of Rumor Spreading Model considering Node Activity in Complex Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Liang’an Huo ◽  
Fan Ding ◽  
Chen Liu ◽  
Yingying Cheng
Keyword(s):  
Complex Networks ◽  
Dynamic Models ◽  
Spreading Rate ◽  
Dynamical Analysis ◽  
Field Equations ◽  
Spreading Process ◽  
Rumor Spreading ◽  
Node Activity ◽  
Activity Rate ◽  
Mean Field Equations

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.

SE2IR Invest Market Rumor Spreading Model Considering Hesitating Mechanism

2019 ◽  
Vol 7 (1) ◽  
pp. 54-69 ◽  
Author(s):  
Hongxing Yao ◽  
Xiangyang Gao
Keyword(s):  
Mean Field ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Field Equations ◽  
Rumor Spreading ◽  
Immunization Strategy ◽  
Scale Free ◽  
Spreading Model ◽  
Mean Field Equations ◽  
The Impact

Abstract According to the actual situation of investor network, a SE2IR rumor spreading model with hesitating mechanism is proposed, and the corresponding mean-field equations is obtained on scale-free network. In this paper, we first combine the theory of spreading dynamics and find out the basic reproductive number R0. And then analyzes the stability of the rumor-free equilibrium and the final rumor size. Finally, we discuss random immune strategies and target immune strategies for the rumor spreading, respectively. Through numerical simulation, we can draw the following conclusions: Reducing the fuzziness and attractiveness of invest market rumor can effectively reduce the impact of rumor. And the target immunization strategy is more effective than the random immunization strategy for the communicators in the invest investor network.

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.

scholarly journals Rumor spreading in complex networks under stochastic node activity

2020 ◽  
Vol 559 ◽  
pp. 125061
Author(s):  
Yingying Cheng ◽  
Liang’an Huo ◽  
Laijun Zhao
Keyword(s):  
Complex Networks ◽  
Rumor Spreading ◽  
Node Activity

Dynamical behaviors and spatial diffusion in a psychologically realistic rumor spreading model

2020 ◽  
Vol 31 (02) ◽  
pp. 2050034
Author(s):  
Yingying Cheng ◽  
Liang’an Huo ◽  
Liang Ma ◽  
Hongyuan Guo
Keyword(s):  
Psychological Factors ◽  
Cumulative Effect ◽  
Threshold Condition ◽  
Field Equations ◽  
Final Size ◽  
Dynamical Mechanism ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Mean Field Equations ◽  
Sensory Intensity

The spread of rumors has caused serious social and economic problems, especially during emergencies. Reducing the harm caused by rumors requires understanding the dynamical mechanism by which they propagate. To include the influence of time-dependent psychological factors, this paper proposes an improved rumor spreading model and derives mean-field equations describing the dynamics of rumor spreading. The psychological factors considered are the attenuation of individual interest, the cumulative effect of memory, and changes in sensory intensity with time. We also obtain the threshold condition of rumor spreading. Numerical simulations are used to verify our theoretical results. It is proved that the extremum of the cumulative effect of memory and the rumor attraction rate are positively correlated with the peak number of rumor spreaders, and negatively with the time required to reach the final rumor size. Time grows geometrically, while sensory intensity grows arithmetically. The initial approval rate of the memory accumulation effect and the stifling mechanism have little effect on the final rumor size. Finally, it is found that increasing the attenuation of interest coefficient reduces the time needed for the rumor to reach its final size.

Dynamical analysis of rumor spreading model in multi-lingual environment and heterogeneous complex networks

2020 ◽  
Vol 536 ◽  
pp. 391-408 ◽  
Author(s):  
Jiarong Li ◽  
Haijun Jiang ◽  
Xuehui Mei ◽  
Cheng Hu ◽  
Guoliang Zhang
Keyword(s):  
Complex Networks ◽  
Dynamical Analysis ◽  
Rumor Spreading ◽  
Spreading Model

scholarly journals Spreading Dynamics of a 2SIH2R, Rumor Spreading Model in the Homogeneous Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Wang ◽  
Feng Qing ◽  
Jian-Ping Chai ◽  
Ye-Peng Ni
Keyword(s):  
Cognitive Abilities ◽  
Information Dissemination ◽  
Rapid Development ◽  
Mean Field ◽  
Special Kind ◽  
Field Equations ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Homogeneous Network ◽  
Mean Field Equations

With the rapid development of social network in recent years, the threshold of information dissemination has become lower. Most of the time, rumors, as a special kind of information, are harmful to society. And once the rumor appears, the truth will follow. Considering that the rumor and truth compete with each other like light and darkness in reality, in this paper, we study a rumor spreading model in the homogeneous network called 2SIH2R, in which there are both spreader1 (people who spread the rumor) and spreader2 (people who spread the truth). In this model, we introduced discernible mechanism and confrontation mechanism to quantify the level of people's cognitive abilities and the competition between the rumor and truth. By mean-field equations, steady-state analysis, and numerical simulations in a generated network which is closed and homogeneous, some significant results can be given: the higher the discernible rate of the rumor, the smaller the influence of the rumor; the stronger the confrontation degree of the rumor, the smaller the influence of the rumor; the larger the average degree of the network, the greater the influence of the rumor but the shorter the duration. The model and simulation results provide a quantitative reference for revealing and controlling the spread of the rumor.

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
Keyword(s):  
Complex Networks ◽  
Dynamical Analysis ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Homogeneous Complex

The influence of oblivion-recall mechanism and loss-interest mechanism on the spread of rumors in complex networks

2019 ◽  
Vol 30 (09) ◽  
pp. 1950075 ◽  
Author(s):  
Wang Jing ◽  
Li Min ◽  
Wang Ya-Qi ◽  
Zhou Zi-Chen ◽  
Zhang Li-Qiong
Keyword(s):  
Mean Field ◽  
Stable State ◽  
Sir Model ◽  
Field Equations ◽  
Independent State ◽  
Rumor Spreading ◽  
Mean Field Equations ◽  
Angle Of View ◽  
Two Parameters ◽  
The Impact

Studying more realistic propagation mechanisms of rumors is crucial to controlling their spreading. Considering the reality of people’s forgetting and losing interest in the process of rumor spreading, the oblivion-recall mechanism and the loss-interest mechanism are both introduced in this paper to construct a novel susceptible-infected-removed (SIR) model. In our SIR model, the forgetting is regarded as an independent state type of the population, and we use the forgetting factor [Formula: see text] and the recall factor [Formula: see text] to characterize the oblivion-recall mechanism. The mean-field equations are established respectively to describe the transmission dynamics of rumors in homogeneous networks and inhomogeneous networks. By performing stable state analysis, the relationship between these two parameters [Formula: see text], [Formula: see text] and the propagation critical thresholds [Formula: see text] is investigated. It shows that [Formula: see text] is directly proportional to [Formula: see text], which indicates that the loss-interest mechanism makes [Formula: see text] exist, and the oblivion-recall mechanism increases the value of [Formula: see text]. Thus, the oblivion-recall mechanism reduces the outbreak probability of rumor spreading. It also reveals that when considering the impact of oblivion-recall mechanism, both the final rumor size and the propagation velocity of rumors decrease. Moreover, in the case of considering the existence of oblivion-recall mechanism, it is still found that the network topology is an important factor affecting the spread of rumors. We wish that our study can offer a new angle of view on the issue of the spread of rumors.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
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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