Enhancing network cluster synchronization capability based on artificial immune algorithm

Chen, Tinggui; Shi, Jiawen; Yang, Jianjun; Li, Gongfa

doi:10.1186/s13673-019-0164-y

Cited by 20 publications

(14 citation statements)

References 34 publications

(34 reference statements)

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“…Colliander [14] pointed out that the individuals' willingness to interact with each other on the web was variable, and this willingness would decrease after being criticized by others. Chen et al [15] established a network that could be used to describe the network synchronization behavior, and the network structure was optimized by using the artificial immune algorithm to improve the network synchronization effect. Kleiner [16] proposed that in a polarized environment, citizens would have a sense of threat and become more actively involved in political decision-making than ever before.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The simulation experiments test the influence of public opinion polarization from two aspects of dynamic conformity and static conformity, revealing the mechanism of polarization. According to the dynamic conformability function ζ i (t) represented by formulas (14) and (15) at time t = 0, the conformity ζ i (0) = 1 − Y i . So in the formula of static conformity, agent i's conformity of all moments, is 1 − Y i , as shown in formulas (18) and (19).…”

Section: The Influence Of Individual Dynamic Conformitymentioning

confidence: 99%

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Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity

Chen

Yang

et al. 2019

Mathematics

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Nowadays, hot issues are likely become bipolar or multipolar after heated discussion on the Internet. This article is focused on the study of the polarization phenomenon and establishes a public opinion polarization model with the considerations of individual heterogeneity and dynamic conformity. At first, this article introduces the dynamic changing function of an individual’s conformity tendency to other’s attitudes in the interaction process. It further defines the influential weight between different interactive individuals, and expands the interactive individual from complete homogeneity to initial attitude heterogeneity, and finally, conformity heterogeneity. Then, through simulation experiments, we find that the degree of changing in individual attitude is limited. That is, it is difficult for the individuals who have one directional attitude at the initial time to change into another opposite attitude through interaction. In addition, individuals with low conformity within a certain threshold are more likely to form polarization. Finally, the rationality and effectiveness of the proposed model are verified by the typical case “Mimeng Event”.

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Section: Literature Reviewmentioning

confidence: 99%

Section: The Influence Of Individual Dynamic Conformitymentioning

confidence: 99%

Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity

Chen

Yang

et al. 2019

Mathematics

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“…Subsequently, a new public opinion polarization model to explore opinion propagation rule is set up. In addition, according to the individual revenue function, a network structure considering node exit mechanism is built on the basis of a BA scale-free network [32], which expands the static network in previous studies [33,34] into the dynamic one, making it more realistic. Finally, the influences of different social preferences and individual revenue functions on the public opinion polarization effects are analyzed through simulation experiments, and a real case is also given to verify the practicability and effectiveness of the proposed model.…”

Section: Literature Reviewmentioning

confidence: 99%

Public Opinion Polarization by Individual Revenue from the Social Preference Theory

Chen

et al. 2020

IJERPH

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Social conflicts occur frequently during the social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinion with the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences, as well as individual revenue on the effect of public opinion polarization, are analyzed through simulation experiments. The simulation results show that (1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case.

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“…Unbounded confidence model (UCM) allows interactions between inconsistent user pairs even with negative feedback. Chen et al used artificial immune algorithm network structure to control the spread and diffusion of network public opinion. Ghalebi et al proposed a new framework to provide a dynamic network model.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and simulation analysis of public opinion polarization in a dynamic network environment

Yuan

Shi

Che

et al. 2020

Concurrency and Computation

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Summary At present, there are many group incidents on the Internet, which has caused a high level of public opinion. Hot issues on the Internet can often trigger intense discussions among netizens and finally evolve toward bipolarization or multipolarization. This article focuses on the phenomenon of public opinion polarization under the dynamic network. First, the single‐dimensional evaluation system for the JA model was expanded to multidimensional and analyzes the main factors that cause the polarization of public opinion of network groups from multiple dimensions of hot events. Second, the strength of the relationship between individuals and its dynamic changes were taken into account in the model. The strength of the relationship between individuals and its changes will affect the interaction of opinions, and thus affect the degree and speed of group polarization. Third, combining the changing process of the strength of relationship, the dynamic connection mechanism of network nodes was set to make the network structure more consistent with the social network in real life. Finally, we verify the reliability and scientificity of the model by combining practical cases.

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Enhancing network cluster synchronization capability based on artificial immune algorithm

Cited by 20 publications

References 34 publications

Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity

Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity

Public Opinion Polarization by Individual Revenue from the Social Preference Theory

Modeling and simulation analysis of public opinion polarization in a dynamic network environment

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