Malicious viruses spreading on complex networks with heterogeneous recovery rate

Long, Linbo; Zhong, Kangping; Wang, Wei

doi:10.1016/j.physa.2018.05.149

Cited by 10 publications

(6 citation statements)

References 38 publications

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“…Long et al [304] considered an SIS model in which the recovery rate of a node i is positively related to the resource provided by its healthy neighbors. That is, γ i = 1 − (1 − γ 0 ) ωR i , with γ 0 being the basic recovery rate and ω a scale factor, where R i is the resource provided by its healthy neighbors and takes the form…”

Section: Effect Of Individual Resourcesmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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Section: Effect Of Individual Resourcesmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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“…These features are susceptible (S), infected (I), and removed (R). In epidemiology, this method was used to evaluate the number of susceptible, sick, and recovered people in a population [13]. Deepened by the study of Zhang et al [14], simulating of infectious diseases is a technique that has been used to research the processes as to how infections are transmitted, forecast an outbreak's potential path, and test approaches for managing an outbreak, according to the authors.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, antivirus software stops a device from being infected with the same virus, but not with all mutated variants [16]. Several studies have been conducted and many mathematical models and strategies have been proposed to at least obstruct the spread the virus on networks, SIR model [4], SIRS model [13], SEIR model [17], SEIQR model [18], SIPS model [19], SAIR model [20], [21], SLBRS [22], SIIRS model [23], delayed model [24] on virus propagation.…”

Section: Introductionmentioning

confidence: 99%

Modeling virus spread on a network using NetLogo for optimum network management

Alimboyong

2021

IJEECS

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<span>The infections in computer networks are complex. Its spread is analogous to a contagious disease which can cause destruction within a few seconds. Viruses in a computer or computer networks can spread rapidly by various means such as access to online social networking sites like twitter, Facebook, and opening of email attachments. Thus, infections can go from being little dangerous to significantly harmful for a network. This paper proposed a simulation model that can predict the propagation of virus including the trend and the average infection rate using NetLogo software. Observed and simulated data sets were validated using chi-square tests. Results of the experiment have demonstrated accurate performance of the proposed model. The model could be very helpful for network administrators in mitigating the virus propagation and obstruct the spread of computer virus other than the usual prevention scheme particularly the use of antivirus software and inclusion of firewall security. </span>

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“…Inspired by the work of Ref. [32], the coupled dynamics of resource allocation and disease spreading on both single and multiplex networks has been widely researched in recent years [33][34][35][36][37]. In spite of a large body of literatures about the interplay between awareness (resource) and epidemics, there is a lack of research on the co-evolutionary mechanism among the three dynamical processes.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Awareness-Driven Individual Resource Allocation on the Epidemic Dynamics

et al. 2020

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We investigate the effects of self-protection awareness on the spread of disease from the aspect of resource allocation behavior in populations. To this end, a resource-based epidemiological model and a self-awareness-based resource allocation model in complex networks are proposed, respectively. First of all, we study the coupled disease-awareness dynamics in complex networks with fixed degree heterogeneity. Through extensive Monte Carlo simulations, we find that overall the self-awareness inhibits the spread of disease. More importantly, the influence of the self-awareness on the spreading dynamics can be divided into three phases. In phase I, the self-awareness is relatively small and the outbreak of the epidemic can not be suppressed effectively. While, in phase II, the epidemic size is significantly reduced. Finally, in phase III, there is a sufficiently large value of self-awareness, the disease cannot outbreak anymore. Further, we study the impact of degree heterogeneity on the coupled disease-awareness dynamics and find that the network heterogeneity plays the role of “double-edged sword” in that it can either suppress or promote the epidemic spreading. Specifically, when the basic infection rate is relatively small, it promotes the spread of disease under the condition that there is a relatively small self-awareness. While, when the basic infection rate is relatively large, it inhibits the outbreak of epidemic at a relatively small self-awareness; in turn, it promotes the outbreak of epidemic at a relatively large self-awareness.

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Malicious viruses spreading on complex networks with heterogeneous recovery rate

Cited by 10 publications

References 38 publications

Coevolution spreading in complex networks

Coevolution spreading in complex networks

Modeling virus spread on a network using NetLogo for optimum network management

Effects of the Awareness-Driven Individual Resource Allocation on the Epidemic Dynamics

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