Modeling of knowledge transmission by considering the level of forgetfulness in complex networks

Cao, Bin; Han, Shuihua; Jin, Zhen

doi:10.1016/j.physa.2015.12.137

Cited by 38 publications

(12 citation statements)

References 23 publications

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“…While a good deal of works have investigated knowledge acquisition (see e.g. [8,[17][18][19][20][21][22]), self-knowledge has received substantially less attention [23]. The present work approached this subject by using powerful concepts derived from network science.…”

Section: Discussionmentioning

confidence: 99%

The dynamics of knowledge acquisition via self-learning in complex networks

Lima

Arruda

Silva

et al. 2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Studies regarding knowledge organization and acquisition are of great importance to understand areas related to science and technology. A common way to model the relationship between different concepts is through complex networks. In such representations, networks' nodes store knowledge and edges represent their relationships. Several studies that considered this type of structure and knowledge acquisition dynamics employed one or more agents to discover node concepts by walking on the network. In this study, we investigate a different type of dynamics adopting a single node as the "network brain." Such a brain represents a range of real systems such as the information about the environment that is acquired by a person and is stored in the brain. To store the discovered information in a specific node, the agents walk on the network and return to the brain. We propose three different dynamics and test them on several network models and on a real system, which is formed by journal articles and their respective citations. The results revealed that, according to the adopted walking models, the efficiency of self-knowledge acquisition has only a weak dependency on topology and search strategy.

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Section: Discussionmentioning

confidence: 99%

The dynamics of knowledge acquisition via self-learning in complex networks

Lima

Arruda

Silva

et al. 2018

Chaos: An Interdisciplinary Journal of Nonlinear Science

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show abstract

“…According to the characteristics of knowledge transmission, knowledge transmission requires a certain amount of contact time or "fixed time" ( Cao, Han, & Jin, 2016 ). In a group, if there are few or even no individuals around with knowledge, such knowledge will be gradually forgotten as time goes by.…”

Section: Literature Reviewmentioning

confidence: 99%

A rumor reversal model of online health information during the Covid-19 epidemic

Wang

et al. 2021

Information Processing & Management

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The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

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“…Zhu and Ma [45] propose a knowledge sharing model considering time-varying information channels and investigated the knowledge evolution in different network structures. Considering the knowledge forgetfulness, Cao et al [46] establish a knowledge transmission model in which the level of forgetfulness depended mainly on the number of neighboring individuals who possess knowledge. Through developing a game model for knowledge-sharing strategies at the interpersonal level, Ho et al [47] assess the dynamics of individual behavior within organizations in terms of knowledge sharing.…”

Section: Knowledge Sharing Behavior Among Enterprisesmentioning

confidence: 99%

Dynamic Evolution of Knowledge Sharing Behavior among Enterprises in the Cluster Innovation Network Based on Evolutionary Game Theory

Kong

Zhu

2019

Sustainability

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Knowledge sharing behavior based on the cluster innovation network has become the primary measure for enterprises to realize sustainable innovation. In order to promote the proactive knowledge sharing behavior among enterprises in the long term, the dynamic evolutionary process and law of knowledge sharing in the network need to be further studied. As different from the hypothesis of the rational man in the classical game theory, this paper establishes an evolutionary game model of knowledge sharing behavior in the cluster innovation network based on the evolutionary game theory, and discusses how the bounded rational enterprises can achieve the evolutionary equilibrium through continuously adaptive learning and strategy optimization, further explores the influence factors on the evolutionary trajectory. Combined with mathematical derivation and simulation analysis, the following results are obtained: over time, the dynamic evolution of knowledge sharing behavior in the cluster innovation network is influenced by initial states of the system, but can always reach the evolutionary stable equilibrium; factors such as synergy revenue have a positive impact on the evolutionary results, while factors such as opportunity interest have a negative impact on the evolutionary results; the factor of revenue distribution has a U-shape relationship with the evolutionary results, and the factor of direct revenue has no effect on the results. The results are expected to have an implication for improving the sustainable innovation development of enterprises in the cluster innovation network.

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Modeling of knowledge transmission by considering the level of forgetfulness in complex networks

Cited by 38 publications

References 23 publications

The dynamics of knowledge acquisition via self-learning in complex networks

The dynamics of knowledge acquisition via self-learning in complex networks

A rumor reversal model of online health information during the Covid-19 epidemic

Dynamic Evolution of Knowledge Sharing Behavior among Enterprises in the Cluster Innovation Network Based on Evolutionary Game Theory

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