Knowledge transmission model with consideration of self-learning mechanism in complex networks

Haiying Wang, Jun Wang, Liting Ding and Wei Wei

Applied Mathematics and Computation, 2017, vol. 304, issue C, 83-92

Abstract: Based on the fact that one can attain knowledge by oneself, which is different from epidemic spreading, we analyze the knowledge transmission in complex networks. In this paper, we propose a knowledge transmission model by considering the self-learning mechanism and derive the mean-field equations that describe the dynamics of the knowledge transmission process. Furthermore, we obtain the transmission threshold R0, which is closely related with the transmission rate and self-learning rate. Moreover, we investigate the global stability of the knowledge free equilibrium E0 and the endemic equilibrium E* of the model. That is, when R0 < 1, the knowledge free equilibrium point E0 is globally asymptotically stable and the knowledge becomes completely extinct eventually; when R0 > 1, a unique endemic equilibrium point E* is globally stable, and the knowledge can be transmitted. Finally, numerical simulations are given to illustrate the theoretical results. The simulation results indicate that the self-learning factor has an obvious promoting effect on the knowledge transmission, both in scale-free and homogeneous networks. Besides, the simulation results illustrate that the scale-free network is more efficient to knowledge transmission.

Keywords: Self-learning; Knowledge transmission; Networks; Transmission threshold; Equilibrium (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:304:y:2017:i:c:p:83-92

DOI: 10.1016/j.amc.2017.01.020

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