Epidemic spreading on random surfer networks with infected avoidance strategy

In this paper, we study epidemic spreading on random surfer networks with infected avoidance （IA） strategy. In particular, we consider that susceptible individuals＇ moving direction angles are affected by the current location information received from infected individuals through a directed information network. The model is mainly analyzed by discrete-time numerical simulations. The results indicate that the IA strategy can restrain epidemic spreading effectively. However, when long-distance jumps of individuals exist, the IA strategy＇s effectiveness on restraining epidemic spreading is heavily reduced. Finally, it is found that the influence of the noises from information transferring process on epidemic spreading is indistinctive.

Dynamic properties of epidemic spreading on finite size complex networks

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS （susceptibleinfected-susceptible） model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Epidemic spreading on networks with vaccination

In this paper, a new susceptible-infected-susceptible （SIS） model on complex networks with imperfect vaccination is proposed. Two types of epidemic spreading patterns （the recovered individuals have or have not immunity） on scale-free networks are discussed. Both theoretical and numerical analyses are presented. The epidemic thresholds related to the vaccination rate, the vaccination-invalid rate and the vaccination success rate on scale-free networks are demonstrated, showing different results from the reported observations. This reveals that whether or not the epidemic can spread over a network under vaccination control is determined not only by the network structure but also by the medicine＇s effective duration. Moreover, for a given infective rate, the proportion of individuals to vaccinate can be calculated theoretically for the case that the recovered nodes have immunity. Finally, simulated results are presented to show how to control the disease prevalence.

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.

一种带隔离机制的SIS模型研究

以往的病毒传播模型很少考虑隔离时间对病毒传播结果的影响,根据实际情况提出一个与时间有关的带隔离机制的病毒传播模型,并在BA无标度网络上对其进行模拟,分析试验结果,解释时间机制对病毒传播的影响。

新个体的加入及初始条件对疾病传播的影响

用复杂网络理论构建人群关系网,采用随机差分方程组等价描述SIS模型与SIR模型的疾病传播过程,通过方程组,理论上得到了某些一般性结论。然后讨论了由于新个体加入使网络变复杂的传播情况,通过仿真发现,新加个体的重要度及人群关系网的拓扑结构对疾病传播过程有影响。此外,研究了SIR模型中不同初始感染个体对疾病传播的影响,当初始个体是随机选取或来自不同k-center时,传播过程有明显的不同。而且对个体数目及平均度相同的网络,在相同条件下,网络的深度越小,感染的人数越多。

赋权网络中的弱化免疫研究

随着复杂网络在社会经济管理领域应用研究的发展,信息扩散或谣言传播及相应的传播控制也得到了人们的广泛关注.本文定义了边赋权网络中的控制行为——弱化免疫,即通过降低免疫节点所关联的边的权值,同时保持免疫后网络节点间的连接畅通,可使整个网络保持一定效率下的正常运行的免疫策略;其次,建立基于病毒传播SI模型上的弱化免疫模型,给出加权网络中弱化免疫下的谣言密度演化规律.最后研究加权网络中基于弱化免疫下的目标免疫策略,并分析弱化免疫网络同步性能,对加权网络中谣言传播和免疫策略等相关参数进行数值模拟,表明理论模型的可行性以及免疫策略的合理性.

复杂网络中的弱化免疫策略分析

针对免疫策略在病毒免疫时会删除网络结构级联边从而出现削弱网络连通效率的问题,提出一种含权网络特定的病毒弱化免疫策略方法。该方法通过构建SI病毒传播模型,给出该模型的病毒感染密度演化公式。理论分析表明:病毒传播率与网络结构的异化性有密切关系,节点度的大小会影响病毒传播的效果,同时弱化免疫策略能衰减连边权值降低传播率,达到遏制病毒传播保留网络连通效率的目的。计算机仿真结果验证了理论模型的可行性和弱化免疫的合理性。最后,将弱化免疫策略应用到局域世界以及目标免疫策略中,更进一步说明了弱化免疫策略能有效控制病毒传播速度。

加权局域网络上的病毒传播行为研究

病毒传播问题的研究一直是国际上科学家所关注的焦点,但是在加权局域网络中的病毒传播研究却是空白。由于实际存在的网络很大一部分是加权局域网络,因此研究了一种特定加权局域网络中的传播行为。采用病毒传播的SI模型,令病毒的传播速度和网络的连接权重正相关。对加权局域网络中病毒传播行为的研究表明:加权局域网络的无标度性质和加权局域世界性质对病毒的传播有深刻的影响。由于加权局域网络能够很好地反应实际世界,因此该研究具有很广的应用背景。

基于侥幸心理的自愿接种模型研究

该文基于个体接种疫苗倾向的差异性,结合博弈理论建立了一个基于节点重要性倾向的自愿接种模型;同时,针对人们在选择是否接种时存在的心理因素,建立了一个基于侥幸心理的自愿接种模型。针对疫苗永久有效、疫苗有效期有限和信息存在时间延迟3种情况,借助经典的SIS传染病模型在BA无标度网络与ER随机网络上对上述两个模型的疾病传播进程进行了对比分析。结果表明:若存在侥幸心理,BA网络中的感染节点密度将更大,即对BA网络影响更大;还发现对于该文所设参数,当人们存在侥幸心理时,感染人数增加了2倍以上。