Individual-based optimal weight adaptation for heterogeneous epidemic spreading networks

Hu, Ping; Ding, Li; Hadzibeganovic, Tarik

doi:10.1016/j.cnsns.2018.04.003

Cited by 37 publications

(18 citation statements)

References 46 publications

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“…From (14) and φ i j (t) := p ii (t)(1 − x * i (t))β j x * j (t), we know that the weight adaptation of player i is based on its own infection, its out-neighbors, and the costate component p ii . The infection of player i and its neighbors are just local information.…”

Section: Numerical Resultsmentioning

confidence: 99%

A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Huang

Zhu

2020

IEEE Trans. Control Netw. Syst.

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Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency for information exchange. However, malware and virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use an SIS epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus-resistance can be achieved by a distributed weight adaptation scheme.

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Section: Numerical Resultsmentioning

confidence: 99%

A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Huang

Zhu

2020

IEEE Trans. Control Netw. Syst.

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“…Sun et al [32] studied the spread of epidemic diseases in adaptively weighted scale-free networks. Hu et al [33] changed the weights of links in an adaptive weighted network to balance the trade-off between the overall infection level and individual weight adaptation cost. Individuals could adapt their contact strengths to inhibit epidemic spread.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the adaptive weighted networks have been increasingly studied in different contexts of epidemics [19,32], social network [31,33], and computer network [18], a rigorous analysis of reliability of the networks capable of rewiring is yet to be delivered in the literature. The impact of rewiring weighted links on the reliability of the networks has not been understood.…”

Section: Related Workmentioning

confidence: 99%

Reliability Analysis of Large-Scale Adaptive Weighted Networks

Song

Wang

et al. 2020

IEEE Trans.Inform.Forensic Secur.

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Disconnecting impaired or suspicious nodes and rewiring to those reliable, adaptive networks have the potential to inhibit cascading failures, such as DDoS attack and computer virus. The weights of disconnected links, indicating the workload of the links, can be transferred or redistributed to newly connected links to maintain network operations. Distinctively different from existing studies focused on adaptive unweighted networks, this paper presents a new mean-field model to analyze the reliability of adaptive weighted networks against cascading failures. By taking mean-field approximation, we develop a new continuous-time Markov model to capture the propagations of cascading failures, and the rewiring actions that individual nodes can take to bypass failed neighbors. We analyze the stability of the model to identify the critical conditions, under which the cascading failures can be eventually inhibited or would proliferate. The conditions are evaluated under different link weight distributions and rewiring strategies. Our model reveals that preferentially disconnecting suspicious peers with high weights can effectively inhibit virus and failures.

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“…Network modeling plays a critical role in identifying structural properties and analyzing contagion processes on networks, such as the spreading of epidemic and malware, as well as the di usion of news and ideas. Meanwhile, it has been acknowledged that the intrinsic characteristic di erences among individuals and the complicated connectivity patterns are two main important factors determining the properties of the contagion process [1][2][3]. For instance, in a social network, individuals are distinguished by their attributes such as gender and age.…”

Section: Introductionmentioning

confidence: 99%

Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

Ding

2019

Complexity

Self Cite

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The complicated interaction patterns among heterogeneous individuals have a profound impact on the contagion process in the networks. In recent years, there has been increasing evidence for the emergence of many-body interactions between two or more nodes in a wide range of biological and social networks. To encode these multinode interactions explicitly, the simplicial complex is now a popular alternative to simple networks. Meanwhile, the time-varying network has been acknowledged as a key ingredient of the contagion process. In this paper, we consider the connectivity pattern of networks affected by the homophily effect associated with individual attributes and investigate the impact of homophily-driven group interactions on the contagion process in temporal networks. The simplicial complex modeling framework is adopted to capture stochastic interactions between passively selected nodes in the paradigm of activity-driven networks. We study the evolution of infection and the epidemic threshold of the contagion process by both analytical and numerical methods. Our results on statistical topological properties of instantaneous network may shed light on accurately characterizing the evolution curve of infection. Furthermore, we show the impact of the homophily-driven interaction pattern on the epidemic threshold, which generalizes the existing results on both the paradigmatic activity-driven network and the simplicial activity-driven network.

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Individual-based optimal weight adaptation for heterogeneous epidemic spreading networks

Cited by 37 publications

References 46 publications

A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Reliability Analysis of Large-Scale Adaptive Weighted Networks

Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

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