Knowledge diffusion in the collaboration hypernetwork

Yang, Guang-Yong; Hu, Zhao-Long; Liu, Jianguo

doi:10.1016/j.physa.2014.10.012

Cited by 54 publications

(33 citation statements)

References 21 publications

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“…In all, our research contributes to present literature in providing a model that can explain how social proximity affects the typology of knowledge network with power-laws degree distribution. Prior studies suggested that actors in a network with more ties have greater advantage in building new ties (Yang et al, 2015). Little attention has been put on how different proximities affect the constructing of knowledge ties during the growth process of a scale-free network.…”

Section: Conclusion and Limitationmentioning

confidence: 99%

The effect of social proximity on degree distribution of Scale-free network

Xi-yao¹,

Zhao²

2019

Proceedings of the International Academic Conference on Frontiers in Social Sciences and Management Innovation (IAFSM 2018)

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With limited resources in knowledge seeking, firms are more likely to connect with influential firms in adjacent areas according to preferential attachment mechanism of BA model, and thus geographical proximity plays an important role in shaping typology of a scale-free network. However, prior researches, adopting BA model, cannot explain why there are more and more nonlocal ties constructed via social relationships of local firms in industrial clusters. This paper proposed a new model by changing the rule of preferential attachment to detect the effect of social proximity on the typology of scale-free network through simulation. The results indicate that the distance of in-degree between core and peripheral nodes would decrease with the expanding of searching range and number of connections affecting by social proximity. Though the degree distribution still follows power-laws, peripheral nodes are less depended on core actors in accessing to external knowledge in case that social proximity rather than geographical proximity becomes the main factor in actors' knowledge seeking strategy.

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Section: Conclusion and Limitationmentioning

confidence: 99%

The effect of social proximity on degree distribution of Scale-free network

Xi-yao¹,

Zhao²

2019

Proceedings of the International Academic Conference on Frontiers in Social Sciences and Management Innovation (IAFSM 2018)

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“…Luo, Du, Liu, Xuan, and Wang (2015) focused on a co-evolution of knowledge diffusion and the social network structure. Yang, Hu, and Liu (2015) further investigated the performance of knowledge diffusion by knowledge absorptive capability and the knowledge diffusion mechanism. In their study, the speed of knowledge diffusion is determined by hypernetwork size, so that the smaller the hypernetwork is, the faster and more easily knowledge in that network diffuses.…”

Section: Knowledge Diffusion Processes and Their Mechanismsmentioning

confidence: 99%

“…Given modern social networks, people are also more willing to trust each other and exchange tacit knowledge (Marrocu, Paci, & Usai, 2013). The network structures of collaborations, hypernetwork sizes, and knowledge evolution mechanisms reflect the growing number of collaborative papers (Yang et al, 2015).…”

Section: Publicationmentioning

confidence: 99%

Configuring the Knowledge Diffusion Policy Portfolio of Higher Education Institutes

Chen

Shyu

Huang

2017

EURASIA J MATH SCI T

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The research proposes an analytic framework to configure the policy portfolio for diffusing knowledge embedded in higher-education institutes. A modified Delphi method was first proposed to derive the key factors for enabling and enhancing the diffusion mechanism. The influence relationships between these factors were derived using the Decision-Making Trial and Evaluation Laboratory (DEMATEL). The analytic network process (ANP) derived weights associated with the key factors. Finally, the Grey Relational Analysis (GRA) was used to derive the policy instruments based on the Grey Relationships between the key factors and the policy instruments. An empirical study based on Taiwanese highereducation institutes also/then verified the feasibility of the proposed analytic framework. These findings suggest that research and development activities, publications, and networks of academic exchange play primary roles in knowledge diffusion in highereducation institutions. Motivation, human resources, and investment are the most important policy instruments to use for improving knowledge diffusion in the highereducation system.

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“…The effect of knowledge dissemination in personalized education is closely related to how we describe the knowledge dissemination process [5, 6]. So far, knowledge dissemination models mainly focus on the learning rules [7], the memory retention [8, 9] and forgetting mechanisms [10, 11].…”

Section: Introductionmentioning

confidence: 99%

Optimal teaching strategy in periodic impulsive knowledge dissemination system

Liu

Wang³

et al. 2017

PLoS ONE

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Accurately describing the knowledge dissemination process is significant to enhance the performance of personalized education. In this study, considering the effect of periodic teaching activities on the learning process, we propose a periodic impulsive knowledge dissemination system to regenerate the knowledge dissemination process. Meanwhile, we put forward learning effectiveness which is an outcome of a trade-off between the benefits and costs raised by knowledge dissemination as objective function. Further, we investigate the optimal teaching strategy which can maximize learning effectiveness, to obtain the optimal effect of knowledge dissemination affected by the teaching activities. We solve this dynamic optimization problem by optimal control theory and get the optimization system. At last we numerically solve this system in several practical examples to make the conclusions intuitive and specific. The optimal teaching strategy proposed in this paper can be applied widely in the optimization problem of personal education and beneficial for enhancing the effect of knowledge dissemination.

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Knowledge diffusion in the collaboration hypernetwork

Cited by 54 publications

References 21 publications

The effect of social proximity on degree distribution of Scale-free network

The effect of social proximity on degree distribution of Scale-free network

Configuring the Knowledge Diffusion Policy Portfolio of Higher Education Institutes

Optimal teaching strategy in periodic impulsive knowledge dissemination system

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