Immunization for scale-free networks by random walker

Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.

Immune Control Strategies for Vaccinia Virus-related Laboratory-acquired Infections

While presenting biological characteristics of vaccinia virus and laboratory-acquired infections during related research processes, this paper focuses on benefits and risks of vaccinia virus immunization in relation to laboratory-acquired infections, describes characteristics and the adaptation of vaccinia virus vaccine, analyses the role vaccinia virus immunization plays in the prevention and control of laboratory-acquired infections, and finally proposes solutions and countermeasures to further promote and implement immune control strategies. The problem related to immune strategy and laboratory- acquired infections which is being raised, analyzed and explored plays an active and instructive role in vaccinia virus related researches and laboratory- acquired infections, and also helps to recommend and develop relevant immune strategy for future vaccine control of such infections.

Random walk immunization strategy on scale-free networks

A novel immunization strategy called the random walk immunization strategy on scale-free networks is proposed. Different from other known immunization strategies, this strategy works as follows： a node is randomly chosen from the network. Starting from this node, randomly walk to one of its neighbor node; if the present node is not immunized, then immunize it and continue the random walk; otherwise go back to the previous node and randomly walk again. This process is repeated until a certain fraction of nodes is immunized. By theoretical analysis and numerical simulations, we found that this strategy is very effective in comparison with the other known immunization strategies.

Correlation and trust mechanism-based rumor propagation model in complex social networks

In real life, the rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, the mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies.The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.

Rumor Spreading Model with Immunization Strategy and Delay Time on Homogeneous Networks

In order to prevent and control the spread of rumors, the implementation of immunization strategies for ignorant individuals is very necessary, where the immunization usually means letting them learn the truth of rumors.Considering the facts that there is always a delay time between rumor spreading and implementing immunization, and that the truth of rumors can also be spread out, this paper constructs a novel susceptible-infected-removed(SIR) model.The propagation dynamical behaviors of the SIR model on homogeneous networks are investigated by using the meanfield theory and the Monte Carlo method. Research shows that the greater the delay time, the worse the immune effect of the immunization strategy. It is also found that the spread of the truth can inhibit to some extent the propagation of rumors, and the trend will become more obvious with the increase of reliability of the truth. Moreover, under the influence of delay time, the existence of nodes' identification force still slightly reduces the propagation degree of rumors.