Knowledge diffusion simulation of knowledge networks: based on complex network evolutionary algorithms
Based on the evolutionary algorithms of the four complex networks, the evolution of knowledge network is regarded as that of complex networks. With the heterogeneity of knowledge level, knowledge absorptive and innovative capacity and agents’ knowledge types considered, theoretical models of knowledge network evolution are constructed. Through numerical simulation, different network structures are analyzed in terms of their effects on the diffusion efficiency of the overall knowledge as well as of various types of knowledge. The simulation results show that: with the diffusion of the overall knowledge considered, although the overall knowledge level in a small-world structure is lower than the random network in the early and middle stage, it is close to the highest one later on; moreover, its growth rate is relatively higher among all four networks and its knowledge levels are distributed most uniformly. With regard to the diffusion of different types of knowledge, the small-world network is proved to produce the most uniform gap between knowledge types and help those dominant industries in the early stage remain advanced during the evolutionary process.
KeywordsKnowledge network Knowledge diffusion Complex network Heterogeneity Knowledge absorptive capacity
This work is partially supported by grants from the National Natural Science Foundation of China (No. 71602012), Chengdu Soft Science Project (No. 2016-RK00-00247-ZF) and Philosophy and Social Science Research Fund Project of Chengdu University of Technology (No. YJ2017-JX003).
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