scholarly journals Controlling Virus Spread Using an Intermittent Quarantine Strategy on Multiplex Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xingguo Li ◽  
Xiaoping Luo ◽  
Yiwu Wang
Keyword(s):  
Information Transmission ◽  
Time Delays ◽  
Information Diffusion ◽  
Mean Field ◽  
Transmission Probability ◽  
Virus Spread ◽  
Spreading Dynamics ◽  
Control Virus ◽  
Optimal Information ◽  
Virus Spreading

Virus spreading on the Internet will negatively affect cybersecurity. An intermittent quarantine immunization strategy to control virus spreading when containing information diffusion is proposed herein. In this model, information and virus spread on different subnetworks and interact with each other. We further develop a heterogeneous mean-field approach with time delays to investigate this model and use Monte Carlo simulations to systematically investigate the spreading dynamics. For a relatively short intermittent period, the optimal information transmission probability of the virus will be significantly suppressed. However, when the intermittent period is extremely long; increasing the probability of information transmission can control the virus spreading as well as suppress the increase in the intermittent period. Finally, it is shown that the average degree of the two subnetworks does not qualitatively affect the spreading dynamics.

scholarly journals Information Spreading on Memory Activity-Driven Temporal Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Linfeng Zhong ◽  
Yu Bai ◽  
Changjiang Liu ◽  
Juan Du ◽  
Weijun Pan
Keyword(s):  
Information Transmission ◽  
Memory Effect ◽  
Real Data ◽  
Simulation Method ◽  
Transmission Probability ◽  
Temporal Networks ◽  
Spreading Dynamics ◽  
Opposite Situation ◽  
Information Spreading ◽  
Markovian Approach

Information spreading dynamics on temporal networks have attracted significant attention in the field of network science. Extensive real-data analyses revealed that network memory widely exists in the temporal network. This paper proposes a mathematical model to describe the information spreading dynamics with the network memory effect. We develop a Markovian approach to describe the model. Using the Monte Carlo simulation method, we find that network memory may suppress and promote the information spreading dynamics, which depends on the degree heterogeneity and fraction of bigots. The network memory effect suppresses the information spreading for small information transmission probability. The opposite situation happens for large value of information transmission probability. Moreover, network memory effect may benefit the information spreading, which depends on the degree heterogeneity of the activity-driven network. Our results presented in this paper help us understand the spreading dynamics on temporal networks.

scholarly journals Immunization of Cooperative Spreading Dynamics on Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jun Wang ◽  
Shi-Min Cai ◽  
Tao Zhou
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Percolation Theory ◽  
Large Degree ◽  
Transmission Probability ◽  
Final State ◽  
The Monte Carlo Method ◽  
Spreading Dynamics ◽  
Opposite Situation ◽  
The Cost

Cooperative spreading dynamics on complex networks is a hot topic in the field of network science. In this paper, we propose a strategy to immunize some nodes based on their degrees. The immunized nodes disable the synergistic effect of cooperative spreading dynamics. We also develop a generalized percolation theory to study the final state of the spreading dynamics. By using the Monte Carlo method, numerical simulations reveal that immunizing nodes with a large degree cannot always be beneficial for containing cooperative spreading. For small values of transmission probability, immunizing hubs can suppress the spreading, while the opposite situation happens for large values of transmission probability. Furthermore, numerical simulations show that immunizing hubs increase the cost of the system. Finally, all numerical simulations can be well predicted by the generalized percolation theory.

Optimal control of a rumor model with group propagation over complex networks

2020 ◽  
pp. 2150035
Author(s):  
Kalaiselvi Myilsamy ◽  
Muthukrishnan Senthil Kumar ◽  
Athira Satheesh Kumar
Keyword(s):  
Optimal Control ◽  
Mobile Networks ◽  
Social Life ◽  
Minimum Principle ◽  
Mean Field Theory ◽  
Mean Field ◽  
Real Life ◽  
Heterogeneous Model ◽  
The People ◽  
Spreading Dynamics

Rumor is an unauthenticated statement that gives significant changes in the social life of the people, financial markets (stocks and trades), etc. By incorporating the dissemination of rumor through groups in social, mobile networks and by considering the people’s cognitive factor (hesitate and forget), a new model on the rumor spreading process is presented in this paper. The spreading dynamics of rumor in homogeneous and heterogeneous networks is analyzed by using mean-field theory. The reproduction number is obtained by using the next-generation matrix. The global stability of the rumor-free equilibrium for the homogeneous and heterogeneous model is proved elaborately. An optimal control problem is developed to minimize the hesitators and infected persons and the existence of optimality is shown using Pontryagin’s Minimum Principle. The hesitating and forgetting mechanism has a great impact on the model and is similar to the real-life. Further, the control parameters work superior in controlling the spreading of rumors. Finally, the numerical results are verified by the analytical results.

scholarly journals Dynamical Analysis of Dual Product Information Diffusion considering Preference in Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Liang’an Huo ◽  
Xiaoxiao Xie
Keyword(s):  
Complex Networks ◽  
Information Diffusion ◽  
Product Information ◽  
Mean Field ◽  
Dynamical Analysis ◽  
Critical Threshold ◽  
Technology Products ◽  
Information Diffusion Model ◽  
And Diffusion ◽  
Theory And Model

With the development of science and technology, products are being updated more and more quickly. Therefore, the diffusion of product information can make people better choose products. It is very meaningful to study the competition and diffusion of multiple product information. In this paper, the dual product information diffusion model with preference was proposed based on the mean-field equation in complex networks. The dynamic of the model was analyzed by the analysis of Markov chains. According to the Monte Carlo simulation mechanism, the critical threshold of product information was obtained. The accuracy of the theory and model results is verified by computer simulation, and the scenarios in which the two products information are mutually promoted and mutually suppressed are simulated.

Sign in / Sign up

Export Citation Format

Share Document