scholarly journals Optimal filtering and control of network information epidemics

2021 ◽  
Vol 69 (2) ◽  
pp. 122-130
Author(s):  
Fangzhou Liu ◽  
Zengjie Zhang ◽  
Martin Buss
Keyword(s):  
Optimal Control ◽  
Information Diffusion ◽  
Complete Solution ◽  
Optimal Filter ◽  
Optimal Filtering ◽  
Separation Principle ◽  
Estimation Errors ◽  
Sis Model ◽  
Stochastic Perturbations ◽  
Network Information

Abstract In this article, we propose an optimal control scheme for information epidemics with stochastic uncertainties aiming at maximizing information diffusion and minimizing the control consumption. The information epidemic dynamics is represented by a network Susceptible-Infected-Susceptible (SIS) model contaminated by both process and observation noises to describe a perturbed disease-like information diffusion process. To reconstruct the contaminated system states, we design an optimal filter which ensures minimized estimation errors in a quadratic sense. The state estimation is then utilized to develop the optimal controller, for which the optimality of the closed-loop system is guaranteed by a separation principle. The designed optimal filter and controller, together with the separation principle, form a complete solution for the optimal control of network information epidemics with stochastic perturbations. Such optimal-filtering-based control strategy is also generalizable to a wider range of networked nonlinear systems. In the numerical experiments on real network data, the effectiveness of the proposed optimal control is validated and confirmed.

The asymptotic behavior of a stochastic multigroup SIS model

2018 ◽  
Vol 11 (03) ◽  
pp. 1850037 ◽  
Author(s):  
Chunyan Ji ◽  
Daqing Jiang
Keyword(s):  
Asymptotic Behavior ◽  
Numerical Simulations ◽  
Stationary Distribution ◽  
Endemic Equilibrium ◽  
Time Behavior ◽  
Long Time Behavior ◽  
Sis Model ◽  
Stochastic Perturbations ◽  
Long Time ◽  
Theoretical Results

In this paper, we explore the long time behavior of a multigroup Susceptible–Infected–Susceptible (SIS) model with stochastic perturbations. The conditions for the disease to die out are obtained. Besides, we also show that the disease is fluctuating around the endemic equilibrium under some conditions. Moreover, there is a stationary distribution under stronger conditions. At last, some numerical simulations are applied to support our theoretical results.

Opinion evolution in society with stubborn agents using Temporal Model for Opinion and Triad Network Formation (TMOTNF)

2021 ◽  
Vol 9 (5) ◽  
Author(s):  
Eeti Jain ◽  
Anurag Singh
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Network Formation ◽  
Threshold Value ◽  
Network Information ◽  
The Social ◽  
Convergence Point ◽  
Temporal Modelling ◽  
Temporal Model ◽  
Opinion Evolution

Abstract Information diffusion is an important part of the social network. Information flows between the individuals in the social networks to shape and update their opinions about various topics. The updated opinion values of them further spread the information in the network. The social network is always evolving by nature, leading to the dynamics of the network. Connections keep on changing among the individuals based on the various characteristics of the networks and individuals. Opinions of individuals may again be affected by the changes in the network which leads to dynamics on the network. Therefore, the co-evolving nature of dynamics on/of the network is proposed. Co-evolving Temporal Model for Opinion and Triad Network Formation is modelled to evaluate the opinion convergence. Some fully stubborn agents are chosen in the network to affect opinion evolution, framing society’s opinion. It is also analysed how these agents can divert the whole network towards their opinion values. When temporal modelling is done using all the three conditions, Triadic Closure, Opinion Threshold value and the Page Rank value over the network, the network does not reach consensus at the convergence point. Various individuals with different opinion values still exist.

Viral Marketing for Public Health Campaigns in Social Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 313-320
Author(s):  
Xiaofeng Wang ◽  
Wei Zhuo ◽  
Qianyi Zhan ◽  
Yuan Liu
Keyword(s):  
Public Health ◽  
Social Networks ◽  
Information Diffusion ◽  
Viral Marketing ◽  
Target Audience ◽  
Health Campaigns ◽  
Public Health Campaign ◽  
Network Information ◽  
Target Network ◽  
Public Health Campaigns

Viral marketing for public health campaigns aims at identifying a group of seed users to maximize the message of public health information in a target social network. Different from traditional viral marketing problems, public health campaigns try to expand social influence in the target network, meanwhile it also focus on their target audience, who are difficult to discover. Meanwhile, besides the target network, users nowadays can also participate many other social networks. Discovering target audience and viral marketing in these networks, referred to as the source networks, can be relatively easier, and the shared users can act as intermediate nodes transmitting information from these networks to the target one. In this paper, we propose to carry viral marketing for public health campaign in the target network in a roundabout way, by selecting seed users from the target and other external networks and influence users through intra- and inter-network information diffusion. To achieve such an objective, a new inter-network information diffusion model IPADH is introduced in this paper. Based on IPADH, cross-network viral marketing framework IMDP is proposed to solve the problem. Extensive experiments are conducted on anti-smoking campaign datasets, and results demonstrate that IMDP can outperform traditional intra-network viral marketing methods with significant advantages.

Optimal control problem in an epidemic disease SIS model with stages and delays

2016 ◽  
Vol 09 (05) ◽  
pp. 1650072 ◽  
Author(s):  
Yongzhen Pei ◽  
Miaomiao Chen ◽  
Xiyin Liang ◽  
Zhumei Xia ◽  
Yunfei Lv ◽  
...  
Keyword(s):  
Optimal Control ◽  
Stage Structure ◽  
Infectious Period ◽  
Optimal Controls ◽  
Epidemic Disease ◽  
Sis Model ◽  
Terminal Time ◽  
Optimality Systems ◽  
Infectious Burden ◽  
Maximal Effort

Based on literature [J. Q. Li, Z. E. Ma and F. Q. Zhang, Stability analysis for an epidemic model with stage structure, J. Appl. Math. Comput. 9 (2008) 1672–1679], incorporating the recovery of the infected population with the length of the infectious periods, a modified epidemic disease SIS model with delay and stage was investigated. First, the criteria keeping stability with delay were given. Next, in order to lower the level of the infected individuals and minimize the cost of treatment, mixed, early and late therapeutic strategies were introduced into our model, respectively. Then we investigated the existence and uniqueness of optimal controls. And then, we expressed the unique optimal control in terms of the solution of the optimality systems. Finally, by numerical simulations, several important results were acquired: (1) The terminal time influenced the early optimal control largely. In detail, for a shorter terminal time it was optimal to initiate treatment with maximal effort at the start of the epidemic and continue treatment with maximal effort until the switch time was arrived. But for a longer terminal time, the maximal treatment effort need not be a prerequisite at the start or end of the epidemic but it was obligatory at the metaphase of the epidemic. (2) For our SIS model, minimizing the total infectious burden of the disease can be achieved by only early optimal treatment tactics. (3) For a disease with a shorter infectious period time, more cost would be spent to control the disease in order to achieve the optimal control objective. Otherwise, a relative lower cost would be to control the disease with a longer infectious period.

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