Traffic-driven SIR epidemic spreading in networks

Pu, Cunlai; Liu, Siyuan; Yang, Xianxia; Xu, Zhiwei; Ji, Zexuan; Yang, Jian

doi:10.1016/j.physa.2015.11.028

Cited by 29 publications

(12 citation statements)

References 35 publications

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“…Second, the traditional infectious disease model divides the population into healthy people, infected people, and cured people. Similarly, in a complete online information dissemination network, users can also be divided into groups of people who do not know the information, people who know that they have not yet chosen to disseminate, people who know and choose to disseminate behavior, and people who are immune to the information [12]. Therefore, this article will study the information dissemination mechanism in online social networks based on the infectious disease model.…”

Section: Dynamics Simulation Of Infectious Diseasesmentioning

confidence: 99%

Research on the Model of Word-of-Mouth Communication in Social Networks Based on Dynamic Simulation

Fan

Liu

et al. 2021

Business Intelligence and Information Technology

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Word-of-mouth communication plays an important role in consumers' purchasing decisions. With the rapid development of the Internet, social networks have become an important way for word-of-mouth communication. This study draws on the dynamic model of the spread of infectious diseases and establishes a model of word-of-mouth communication in social networks. By analyzing the characteristics of word-of-mouth communication in social networks, the process of withdrawing from the communicator group and becoming a communicator again is introduced. This article uses a dynamic simulation method to simulate and analyze the process of word-of-mouth communication in social networks. The experimental results show that the withdrawal of the communicator group into a communicator again can effectively increase the time of word-of-mouth communication in social networks and reduce the rate of decrease in the popularity of word-of-mouth communication. The simulation analysis of word-of-mouth communication in social networks provides theoretical support for enterprises to make product promotion decisions.

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Section: Dynamics Simulation Of Infectious Diseasesmentioning

confidence: 99%

Research on the Model of Word-of-Mouth Communication in Social Networks Based on Dynamic Simulation

Fan

Liu

et al. 2021

Business Intelligence and Information Technology

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“…In the susceptible-infected-susceptible (SIS) model [4], infected individuals can recover from the disease and become susceptible individuals again. While in the susceptible-infected-recovered (SIR) model [24], infected individuals no longer get infected after recovery from the disease, which are assumed to get the permanent immunity.…”

Section: Epidemiological Dynamicsmentioning

confidence: 99%

Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

2019

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With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.

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Section: Introductionmentioning

confidence: 99%

“…The expansion of these infrastructures and the increasing interconnection between them make it hard for us to understand and control these infrastructures. As a consequence we encountered so many network problems, such as the attacks on the Internet [1], large-scale spreading of computer viruses [2,3,4,5], network congestion [6,7], and many others.One of the fundamental issues is what's the maximum amount of flow a communication network can carry [8]. In the past decade, researchers from the area of network science focused on discussing the delivery capacity of various complex networks based on the methods from statistical physics [9].…”

mentioning

confidence: 99%

Information transmission on hybrid networks

Chen

Wei²,

et al. 2018

Physica A: Statistical Mechanics and its Applications

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Many real-world communication networks often have hybrid nature with both fixed nodes and moving modes, such as the mobile phone networks mainly composed of fixed base stations and mobile phones. In this paper, we discuss the information transmission process on the hybrid networks with both fixed and mobile nodes. The fixed nodes (base stations) are connected as a spatial lattice on the plane forming the information-carrying backbone, while the mobile nodes (users), which are the sources and destinations of information packets, connect to their current nearest fixed nodes respectively to deliver and receive information packets. We observe the phase transition of traffic load in the hybrid network when the packet generation rate goes from below and then above a critical value, which measures the network capacity of packets delivery. We obtain the optimal speed of moving nodes leading to the maximum network capacity. We further improve the network capacity by rewiring the fixed nodes and by considering the current load of fixed nodes during packets transmission. Our purpose is to optimize the network capacity of hybrid networks from the perspective of network science, and provide some insights for the construction of future communication infrastructures.

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Traffic-driven SIR epidemic spreading in networks

Cited by 29 publications

References 35 publications

Research on the Model of Word-of-Mouth Communication in Social Networks Based on Dynamic Simulation

Research on the Model of Word-of-Mouth Communication in Social Networks Based on Dynamic Simulation

Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

Information transmission on hybrid networks

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