Acquaintance Immunization Research Articles

Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks

Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks

Acquaintance immunization with limited knowledge of network structure

Optimal and efficient immunization of large networks remains a challenging task. Many theories and approaches have been suggested, however most of them require complete knowledge of the underlying network structure. Here, we study a targeted immunization strategy that incorporates the fact that there is often limited knowledge on the network structure. Previous work has suggested ‘acquaintance’ immunization, where rather than selecting a random individual to immunize, an individual is selected and then one of their acquaintances is immunized. Here, we generalize acquaintance immunization to the case where rather than selecting a random acquaintance, we examine the degrees of n acquaintances and immunize the one with the highest degree. We develop and solve an analytic framework for this model and verify our model with extensive numerical simulations. We determine the critical percolation threshold pc and the size of the giant component, , for arbitrary degree distributions. We also consider our immunization strategy on real-world networks and determine the variation of pc with increasing n. We find that our new approach improves on both acquaintance immunization and random immunization using limited knowledge.

Global stability and control strategies of a SIQRS epidemic model with time delay

The existing research models of SIQRS are often defined by linear incidence, ignoring the impact of lagging public awareness during outbreaks of infectious diseases. Their research results often deviate from the actual situation. For decision‐makers, the delay of quarantine measures according to its formulation is liable to bring losses. Given this, we establish a new class of SIQRS models based on the nonlinear incidence and focus on the effect of time delay on the propagation characteristics of the system. Firstly, we calculate the basic reproduction number of the transmission dynamics model to obtain the expression of the transmission threshold. Secondly, by constructing suitable Lyapunov functions, we prove the stability of the disease‐free equilibrium point and endemic equilibrium point of the system. Thirdly, we add model immune parameters to study three different immunization strategies, namely, uniform immunization, targeted immunization, and acquaintance immunization. Studies show that compared with uniform immunization, targeted and acquaintance immunization have better control effects. When the immunization ratio increases, the number of infected people decreases, and immune factors can control the spread of the disease. Meanwhile, we calculate the optimal quarantine level value to solve the problem of financial costs in quarantine measures to control infectious diseases. Finally, we verify conclusions of the theoretical research by numerical simulation and further discover that when an infection is in the high‐density outbreak, the time delay will lead to a slower convergence rate of the disease. And with the increase of time delay, the control cost will also increase gradually.

Modeling the dynamics of rumor diffusion over complex networks

Rumor propagation on complex networks is rapidly affecting people’s life. As we all know, the regulatory control of rumors by regulators has a specific impact on the spread of rumors. Limited regulatory resources may saturate the regulatory level. Therefore, in this paper, we have introduced a saturation treatment function to model this phenomenon and further establish an SIS rumor propagation model with consideration of some comprehensive influence on rumor diffusion. First of all, we prove the boundedness of solutions, and the basic reproduction number R0 is obtained by the method of the next generation matrix. Secondly, by constructing Lyapunov function and applying the linearization method of differential equations, the stability conditions of the equilibrium points are derived. Further, we determine the condition for the backward bifurcation of the rumor propagation model. Moreover, in order to control the spread of rumors, we propose targeted immunization control, acquaintance immunization control and optimal control strategies based on complex networks. Finally, the sensitivity analysis of the basic reproduction number is carried out, and the correctness of the theoretical results is verified by numerical simulations. Our results may provide us with useful insights into the dynamics of online rumor propagation. For example, the basic reproduction number R0 gives the threshold of rumor propagation, which provides control conditions for suppressing the spread of rumors. Stability analysis indicates the likelihood of local or global outbreaks for rumors. In addition, the comparison of the effect for control strategies gives a variety of options for the control method.

Defense against malware propagation in complex heterogeneous networks

Devising appropriate defense strategies against malware propagation in complex networks with minimal budget is a challenging problem in research community. This paper studies and compares various immunization strategies such as random immunization, targeted immunization, acquaintance immunization and high-risk immunization to prevent the outbreak of malware. Also, three measures of node centrality (degree, closeness and betweenness) are taken into targeted immunization to slow down the malware propagation process. The malware propagation is modelled based on the susceptible–exposed–infected–recovered–susceptible with quarantine state (SEIRS-Q) epidemic model. Using numerical simulations, the model is verified with considering defense mechanisms in a synthetic (SFN) and a real (Facebook) network topology. The simulation results can help to better understand the effects of defense strategies against the malware propagation. The results show that the use of immunization and software diversity together are more effective than using each of them singly, in terms of reducing the density of infected node and halting malware propagation.

Immunization strategies in networks with missing data.

Network-based intervention strategies can be effective and cost-efficient approaches to curtailing harmful contagions in myriad settings. As studied, these strategies are often impractical to implement, as they typically assume complete knowledge of the network structure, which is unusual in practice. In this paper, we investigate how different immunization strategies perform under realistic conditions-where the strategies are informed by partially-observed network data. Our results suggest that global immunization strategies, like degree immunization, are optimal in most cases; the exception is at very high levels of missing data, where stochastic strategies, like acquaintance immunization, begin to outstrip them in minimizing outbreaks. Stochastic strategies are more robust in some cases due to the different ways in which they can be affected by missing data. In fact, one of our proposed variants of acquaintance immunization leverages a logistically-realistic ongoing survey-intervention process as a form of targeted data-recovery to improve with increasing levels of missing data. These results support the effectiveness of targeted immunization as a general practice. They also highlight the risks of considering networks as idealized mathematical objects: overestimating the accuracy of network data and foregoing the rewards of additional inquiry.

Dynamic stability of an SIVS epidemic model with imperfect vaccination on scale-free networks and its control strategy

Vaccination is the most effective and extensive method to prevent and control the spread of infectious diseases. In this paper, we develop an SIVS epidemic model with demographics and variable population size on scale-free networks. Firstly, we derive two threshold parameters R0 and R˜0 with death rate, and present existence conditions of equilibria. Not only the global asymptotical stability of the disease-free equilibrium is obtained for R˜0<1, but also the global attractivity of the unique endemic equilibrium is proved for R0 > 1. Secondly, the effects of various immunization schemes including uniform immunization, targeted immunization and acquaintance immunization are compared, and the optimal vaccination strategy is analyzed by Pontryagin Maximum Principle. Finally, we perform numerical simulations to verify the correctness of the theoretical results.

Nonmassive immunization to contain spreading on complex networks.

Optimal strategies for epidemic containment are focused on dismantling the contact network through effective immunization with minimal costs. However, network fragmentation is seldom accessible in practice and may present extreme side effects. In this work, we investigate the epidemic containment immunizing population fractions far below the percolation threshold. We report that moderate and weakly supervised immunizations can lead to finite epidemic thresholds of the susceptible-infected-susceptible model on scale-free networks by changing the nature of the transition from a specific motif to a collectively driven process. Both pruning of efficient spreaders and increasing of their mutual separation are necessary for a collective activation. Fractions of immunized vertices needed to eradicate the epidemics which are much smaller than the percolation thresholds were observed for a broad spectrum of real networks considering targeted or acquaintance immunization strategies. Our work contributes for the construction of optimal containment, preserving network functionality through nonmassive and viable immunization strategies.

Immunization strategies in directed networks.

Many complex systems can be modeled as directed networks, which can be regarded as a generalization of undirected networks. In this paper, epidemic dynamics and immunization strategies in directed networks are studied. First, a Susceptible-Infected-Susceptible (SIS) model on a directed network is established employing the mean-field method, and its dynamics and epidemic threshold of the network are studied. Then based on the continuous degree technique, namely, considering the degree of a node as a continuous variable, we propose a method to calculate the epidemic threshold of the immunized network. Besides, some immunization strategies, including optimal immunization, random immunization, combined targeted immunization, and combined acquaintance immunization, and three special networks are considered. Finally, through numerical analysis, all immunization strategies are simulated and compared on different types of networks. We find that the nodes with the largest product of in-degree and out-degree are the most worthy of being immunized.

Dynamical analysis and control strategies of an SIVS epidemic model with imperfect vaccination on scale-free networks.

Vaccination is an effective method to prevent the spread of infectious diseases. In this paper, we develop an SIVS epidemic model with degree-related transmission rates and imperfect vaccination on scale-free networks. Firstly, we derive two threshold parameters and existence conditions of multiple endemic equilibria. Secondly, not only the global asymptotical stability of disease-free equilibrium and the persistence of the disease are derived, but also the global attractivity of the unique endemic equilibrium is proved using the monotone iterative technique. Thirdly, the effects of various immunization schemes including uniform immunization, targeted immunization and acquaintance immunization are studied, and the optimal vaccination strategy is analyzed by Pontryagin’s maximum principle. Finally, we perform numerical simulations to verify these theoretical results.

An incentive-based protection and recovery strategy for secure big data in social networks

Big data sources, such as smart vehicles, IoT devices, and sensor networks, differ from traditional data sources in both output volume and variety. Big data is usually stored on various types of network nodes, which is prone to data security and privacy problems, such as virus infection. In particular, the spread of viruses through social networks can cause large-scale destruction and privacy leakage in the network. This paper aims to provide a solution to protect the security of big data. First, the users are divided into five states according to their reactions to data virus: susceptible, contagious, doubt, immune, and recoverable. Then, we propose a novel model for studying the propagation mechanism of data virus. To control the spread of virus and protect data security, an incentive mechanism is introduced. After that, a protection and recovery strategy (PRS) is developed to reduce infected users and increase the immunized. The experimental results on two data sets indicate that our model gives a good description of the data virus propagation process, and PRS is better than both acquaintance immunization and target immunization methods, which validates the privacy preserving strategy for big data in networks.

A risk defense method based on microscopic state prediction with partial information observations in social networks

The development of network science has led to an increase in the size and user number of social networks. Messages (e.g., rumors, leaked user information) will quickly spread to social networks and lead to terrible results. Researchers have proposed a number of protection methods in risks’ propagation process, such as blocking pivotal topological nodes, controlling the bridges between social communities, etc. However, these methods mainly focus on static topological characteristics of the networks and rarely take the spatio-temporal diffusion dynamic of risks into consideration. In fact, if the selected controlled nodes or bridges are far enough away from the risk source or have already undergone the risks before, they cannot actually affect the risk propagation process at current time. To solve this problem, we propose a microscopic risk diffusion model and aim to defend against network risks and threats by predicting their dynamic propagation from the microscopic probability perspective and collecting the infection boundary nodes that are currently most likely to be contagious state. Meanwhile, in real life, we often fail to obtain the monitoring data of all network nodes, so we use the sensor observation and assume that there are some short propagation paths that are clear to us. We experimentally demonstrate that the estimations of proposed microscopic diffusion model fairly accurately predict the propagation behaviors of the network risks. Moreover, on average, the proposed risk elimination solution based on microscopic state prediction with partial observations outperforms acquaintance immunization and targeted immunization approaches in terms of defense effects and by approximately 30% in terms of defense cost.

Nonlinear dynamical analysis and control strategies of a network-based SIS epidemic model with time delay

• A network epidemic model with nonlinear incidence rate and time delay is regarded. • Stability is proved in a strict mathematical way. • Four different control strategies are investigated and compared in this paper. • Numerical simulations provide a new insight into epidemic diffusion on complex networks. In this paper, we establish a susceptible-infected-susceptible (SIS) epidemic model with nonlinear incidence rate and time delay on complex networks. Firstly, according to the existence of a positive equilibrium point, we work out the threshold values R 0 and λ c of disease propagation. Secondly, we demonstrate the stabilities of the disease-free equilibrium point and the disease-spreading equilibrium point by constructing Lyapunov function and applying delay differential equations theorem. Thirdly, four different control strategies are investigated and compared, including uniform immunization control, acquaintance immunization control, active immunization control and optimal control. Finally, we perform representative numerical simulations to illustrate the theoretical results and further discover that the nonlinear incidence rate can more accurately reflect individual psychological activities when a certain disease outbreaks at a high level.

The epidemic network construction and immunization based on node strength

In this paper, a new spreading network was constructed and the corresponding immunizations were proposed. The social ability of individuals in the real human social networks was reflected by the node strength. The negativity and positivity degrees were also introduced. And the edge weights were calculated by the negativity and positivity degrees, respectively. Based on these concepts, a new asymmetric edge weights scale-free network which was more close to the real world was established. The comparing experiments indicate that the proposed immunization is priority to the acquaintance immunization, and close to the target immunization.

Big Data and Fans Economy Oriented Star Node Mining Improved Algorithm in Social Networks Based on Acquaintance Immunization

With the booming and development of modern technologies in all kinds of fields, the scale of social networks is getting larger and larger, and it is more and more difficult to mine star nodes from this kind of network. On the basis of analyzing the relations among big data, fans economy and star nodes in social network in detail, aiming at the deficiencies of a star node mining algorithm proposed by us, a big data and fans economy oriented improved algorithm based on acquaintance immunization is put forward. Multiple contrast experiments on Karate club network and Dolphin social network demonstrate that the improved algorithm is superior to the old one from the quantity, degree and influence aspects of the mined star nodes.

An Immunization Strategy for Hidden Populations

Hidden populations, such as injecting drug users (IDUs), sex workers (SWs) and men who have sex with men (MSM), are considered at high risk of contracting and transmitting infectious diseases such as AIDS, gonorrhea, syphilis etc. However, public health interventions to such groups are prohibited due to strong privacy concerns and lack of global information, which is a necessity for traditional strategies such as targeted immunization and acquaintance immunization. In this study, we introduce an innovative intervention strategy to be used in combination with a sampling approach that is widely used for hidden populations, Respondent-driven Sampling (RDS). The RDS strategy is implemented in two steps: First, RDS is used to estimate the average degree (personal network size) and degree distribution of the target population with sample data. Second, a cut-off threshold is calculated and used to screen the respondents to be immunized. Simulations on model networks and real-world networks reveal that the efficiency of the RDS strategy is close to that of the targeted strategy. As the new strategy can be implemented with the RDS sampling process, it provides a cost-efficient and feasible approach for disease intervention and control for hidden populations.

Immunity of multiplex networks via acquaintance vaccination

How to find the effective approach of immunizing a population is one open question in the research of complex systems. Up to now, there have been a great number of works focusing on the efficiency of various immunization strategies. However, the majority of these existing achievements are limited to isolated networks, how immunization affects disease spreading in multiplex networks seems to need further exploration. In this letter, we explore the impact of the acquaintance immunization in multiplex networks, where two kinds of immunization strategies, multiplex node-based acquaintance immunization and layer node-based acquaintance immunization, are proposed. With the generating function method, our theoretical framework is able to accurately calculate the critical immunization threshold which is one of the most important indexes to predict the epidemic regime. Moreover, we further uncover that, with the increment of degree correlation between network layers, the immunization threshold declines for multiplex node-based acquaintance immunization, but slowly increases for layer node-based acquaintance immunization.

An improved acquaintance immunization strategy for complex network

The acquaintance immunization strategy is a common strategy to suppress epidemic on complex network which achieves a seemingly perfect balance between cost and effectiveness compared with other canonical immunization strategies. However, the acquaintance immunization strategy fails to take the time-varying factor and local information of nodes into consideration, which limits its effectiveness in some specific network topology. Our improved immunization strategy is based on a new mathematical model Network Structure Index (NSI), which digs deep to measure the connection property and surrounding influence of a node׳s neighbor nodes to better determine the importance of nodes during immunization. Both mathematical derivation and the simulation program tested on various network topology support our idea that this improved acquaintance immunization strategy protects more nodes from infection and immunizes important nodes more efficiently than the original strategies. As to say, our strategy has more adaptability and achieves a more reasonable balanced point between cost and effectiveness.

Study on a model of topology evolution of wireless sensor networks among cluster heads and its immunization

A new evolving network model based on complex network theory among the cluster heads is proposed to study the influence of node failure on the performance of wireless sensor network. According to the proposed model, we discuss the virus immunization strategies, and present a new immune mechanism. Theoretical analysis shows that such an evolving network not only has strong fault tolerance, but also can effectively avoid node premature death caused by the rapid energy depletion. It is also found that if the global network information is unknown, the proposed immunization strategies in this paper can obtain better immune effect than the random immunization and acquaintance immunization strategies. The theoretical analysis is verified by numerical simulations.

The propagation and inhibition of rumors in online social network

In this paper, a new rumor spreading SEIR model is proposed with considering both the spreading characteristics of rumors in the real online social network and the epidemic dynamics models with latency. First, the dynamical evolution equations based on the SEIR model are established. Then an efficient immunization strategy—important acquaintance immunization strategy, is given for the inhibition of rumor spreading. Finally, based on the user datasets of Facebook, the SEIR model and its dynamical evolution equations with various immunization strategies including important acquaintance immunization strategy are utilized in the computer simulation for the evolution process of rumor spreading before and after immunization. Simulation results show that SEIR model conforms to propagation characteristics of the real online social network, and the important acquaintance immunization strategy is an optimal scheme to solve the inhibition of rumor spreading in the online social network.