Modeling cyber rumor spreading over mobile social networks: A compartment approach

Liu, Wanping; Xiao, Wenjun; Yang, Wu; Zhu, Xiaofei; Zhong, Shouming

doi:10.1016/j.amc.2018.09.048

Cited by 62 publications

(47 citation statements)

References 37 publications

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“…It can effectively describe the diffusion environment of user interaction [4,34]. Prior studies constructed the consumers' relationship network based on agent-based modeling and social network analysis, helping firms predict product proliferation in different networks [35,36]. Then, Choi and Byungtae pointed out that the external network perceived by the individual should be the local network itself.…”

Section: Social Network Structure and Social Influencementioning

confidence: 99%

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Zhou

Lin

et al. 2020

Sustainability

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Mobile application innovation diffusion can be used to enhance the reputation and competitiveness of Internet enterprises. However, few works have explored the process of mobile application innovation diffusion from the individual perspective. Therefore, this paper employs multi-agent methods to simulate the innovation diffusion of mobile applications from the perspective of social networks. Specifically, we combine psychology, sociology, game theory and network effect theory to model user behaviors of adoption and rejection decisions for mobile applications. The multi-agent model was built in Anylogic 8 to simulate the communication and interaction between individual users. Then, this paper discusses the evolution of decision-making of social network user groups with different network structures and network effects. We also investigate the impact of different firms' promotion on innovation diffusion. Our findings suggest firms could make better strategies and achieve better diffusion effects from mobile applications. IntroductionNowadays, market competition in mobile applications is increasingly fierce. The success of the mobile application in market promotion not only helps Internet enterprises to seize the market and create business value, but also allows firms establish a good reputation and gain a competitive advantage [1][2][3].The innovation diffusion of mobile applications always occurs in user groups. Since users can communicate and interact with others, most of the diffusion of mobile applications is based on each user in a social network [4,5]. Numerous studies indicate that the development of mobile internet and technology social networks, significantly reducing the cost of communication between users, makes communication and interaction very convenient [6,7]. As various mobile applications allow users to share happiness and dissatisfaction with others on the Internet, users' decisions on mobile applications are also influenced by social relations and social network structure [8,9]. In addition, research evidence also supports the fact that there are network effects in the diffusion of mobile applications; that is, the utility of mobile applications to users will increase with an increase in the number of applications [10]. Specifically, network effects can be divided into global and local network effects. Global network effect means that, when more users adopt innovative technology in the global environment, the value of mobile applications to users will be higher. While the local network effect refers to the effect of related users on the utility value, which emphasizing the interaction between users. The decision-making of users will largely depend on the decision-making of their surroundings. As the number of users adopting mobile applications increases, their neighbors also would like to adopt the same applications. Then, the value and utility of the mobile applications to the users will also increase, which ultimately Sustainability 2020, 12, 2884 2 of 17 affects user decision-making. In additio...

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Section: Social Network Structure and Social Influencementioning

confidence: 99%

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Zhou

Lin

et al. 2020

Sustainability

View full text Add to dashboard Cite

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“…A er years of development, there has formed a new applied mathematics named malware propagation theory, which is devoted to the modeling and study of malware propagation. e earlier malware propagation models build on homogeneously mixed networks [7][8][9][10][11][12][13][14][15] or some special inhomogeneous networks such as scale-free networks [16][17][18]. In view of the structural diversity of real-world networks, it is essential to model and study malware propagation on arbitrary networks.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of a Static Patching Strategy against Computer Viruses

Huang

Yang

et al. 2020

Complexity

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To cope with evolving computer viruses, antivirus programs must be periodically updated. Due to the limited network bandwidth, new virus patches are typically injected into a small subset of network nodes and then forwarded to the remaining nodes. A static patching strategy consists of a fixed patch injection rate and a fixed patch forwarding rate. This paper focuses on evaluating the performance of a static patching strategy. First, we introduce a novel autonomous node-level virus-patch propagation model to characterize the effect of a static patching strategy. Second, we show that the model is globally attracting, implying that regardless of the initial expected state of the network, the expected fraction of the infected nodes converges to the same value. Therefore, we use the asymptotic expected fraction of the infected nodes as the measure of performance of a static patching strategy. On this basis, we evaluate the performances of a few static patching strategies. Finally, we examine the influences of a few parameters on the performance of a static patching strategy. Our findings provide a significant guidance for restraining malware propagation.

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“…Zhou et al proposed the rumor spreading model thought differentiated the unequal footings and taking the influence of topological structure in the model [13]. Boccaletti et al reviewed the main studies on the network structure effects on rumor spreading dynamics from non-linear science to biology, from statistical mechanics to medicine and engineering [14]. Liu et al developed the mathematical rumor compartmental model by considered the rumor-denier's behavior of refuting rumors [15].…”

Section: Introductionmentioning

confidence: 99%

Dynamical Analysis of Rumor Spreading Model With Incubation Mechanism and Activity of Nodes

et al. 2019

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In this paper, incubation mechanism and activity of nodes are introduced to study the dynamics mechanism of rumor spreading in complex network. It is assumed that when the susceptible interacts with the rumor believers, the susceptible need time to think about the rumor before they believe it. The susceptible individual will first go through a incubation period after being infected before becoming believer or stifler. The nodes have active and the inactive states, it can convert each other with a certain probability, the active nodes can interact with others which directs linking, and the inactive nodes just response the active neighbors. The dynamic rumor spreading model was built by mean-field equations in heterogeneous network, and then the numerical simulations were proposed which reveals that the joint efforts of activity rate and spreading rate have negative effects on final rumor spreading size, and the simulations results also confirm the theoretical analysis in the modified model. INDEX TERMS Rumor spreading, incubation mechanism, activity of nodes, heterogeneous networks.

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Modeling cyber rumor spreading over mobile social networks: A compartment approach

Cited by 62 publications

References 37 publications

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System

Evaluating the Performance of a Static Patching Strategy against Computer Viruses

Dynamical Analysis of Rumor Spreading Model With Incubation Mechanism and Activity of Nodes

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