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Knowledge diffusion simulation of knowledge networks: based on complex network evolutionary algorithms

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Abstract

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.

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References

  1. Wei, Q., Gu, X.: Knowledge networks formation and interchain coupling of knowledge chains. J. Appl. Sci. 13(20), 4181–4187 (2013)

    Article  Google Scholar 

  2. Goldenberg, A., Zheng, A.X., Fienberg, S.E., Airoldi, E.M.: A survey of statistical network models. Found. Trends Mach. Learn. 2(2), 129–233 (2010)

    Article  Google Scholar 

  3. Raducha, T., Gubiec, T.: Coevolving complex networks in the model of social interactions. Phys. A 471, 427–435 (2017)

    Article  Google Scholar 

  4. Luo, S., Du, Y., Liu, P., Xuan, Z., Wang, Y.: A study on coevolutionary dynamics of knowledge diffusion and social network structure. Expert Syst. Appl. 42(7), 3619–3633 (2015)

    Article  Google Scholar 

  5. Luo, S., Du, Y., Liu, P., Xuan, Z., Wang, Y.: A study on coevolutionary dynamics of knowledge diffusion and social network structure. Expert Syst. Appl. 42(7), 3619–3633 (2015)

    Article  Google Scholar 

  6. Long, W., Guan, L., Shen, J., Song, L., Cui, L.: A complex network for studying the transmission mechanisms in stock market. Phys. A 484, 345–357 (2017)

    Article  Google Scholar 

  7. Li, H., An, H., Fang, W., Wang, Y., Zhong, W., Yan, L.: Global energy investment structure from the energy stock market perspective based on a heterogeneous complex network model. Appl. Energy 194, 648–657 (2017)

    Article  Google Scholar 

  8. Li, T., Ma, J.: The complex dynamics of R&D competition models of three oligarchs with heterogeneous players. Nonlinear Dyn. 74(1–2), 45–54 (2013)

    Article  MathSciNet  Google Scholar 

  9. Bogliacino, F., Pianta, M.: Profits, R&D, and innovation—a model and a test. Ind. Corp. Chang 22(3), 649–678 (2012)

    Article  Google Scholar 

  10. Lin, J., Ban, Y.: The evolving network structure of US airline system during 1990–2010. Phys. A 410, 302–312 (2014)

    Article  Google Scholar 

  11. Jia, T., Jiang, B.: Building and analyzing the US airport network based on en-route location information. Phys. A 391(15), 4031–4042 (2012)

    Article  Google Scholar 

  12. Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics and endemic states in complex networks. Phys. Rev. E 63(6), 066117 (2001)

    Article  Google Scholar 

  13. Barthélemy, M., Barrat, A., Pastor-Satorras, R., Vespignani, A.: Dynamical patterns of epidemic outbreaks in complex heterogeneous networks. J. Theor. Biol. 235(2), 275–288 (2005)

    Article  MathSciNet  Google Scholar 

  14. Beckmann, M.J.: Economic models of knowledge networks. In: Networks in Action, pp. 159–174. Springer, Berlin (1995)

    Chapter  Google Scholar 

  15. Kobayashi, K. Knowledge Network and Market Structure: An Analytical Perspective. In: Networks in Action, pp. 127–158. Springer, Berlin (1995)

    Chapter  Google Scholar 

  16. Chen, C., Hicks, D.: Tracing knowledge diffusion. Scientometrics 59(2), 199–211 (2004)

    Article  Google Scholar 

  17. Luo, S., Du, Y., Liu, P., Xuan, Z., Wang, Y.: A study on coevolutionary dynamics of knowledge diffusion and social network structure. Expert Syst. Appl. 42(7), 3619–3633 (2015)

    Article  Google Scholar 

  18. Lööf, H., Broström, A.: Does knowledge diffusion between university and industry increase innovativeness? J. Technol. Transf. 33(1), 73–90 (2008)

    Article  Google Scholar 

  19. Cowan, R., Jonard, N.: Network structure and the diffusion of knowledge. J. Econom. Dyn. Control 28(8), 1557–1575 (2004)

    Article  MathSciNet  Google Scholar 

  20. Lin, M., Li, N.: Scale-free network provides an optimal pattern for knowledge transfer. Phys. A 389(3), 473–480 (2010)

    Article  Google Scholar 

  21. Zhou, W., Jia, Y.: Predicting links based on knowledge dissemination in complex network. Phys. A 471, 561–568 (2017)

    Article  Google Scholar 

  22. Liu, J.G., Zhou, Q., Guo, Q., Yang, Z.H., Xie, F., Han, J.T.: Knowledge diffusion of dynamical network in terms of interaction frequency. Sci. Rep. 7(1), 10755 (2017)

    Article  Google Scholar 

  23. Wang, H., Wang, J., Ding, L., Wei, W.: Knowledge transmission model with consideration of self-learning mechanism in complex networks. Appl. Math. Comput. 304, 83–92 (2017)

    MathSciNet  MATH  Google Scholar 

  24. Darr, E.D., Kurtzberg, T.R.: An investigation of partner similarity dimensions on knowledge transfer. Organ. Behav. Hum. Decis. Process. 82(1), 28–44 (2000)

    Article  Google Scholar 

  25. Szulanski, G.: The process of knowledge transfer: a diachronic analysis of stickiness. Organ. Behav. Hum. Decis. Process. 82(1), 9–27 (2000)

    Article  Google Scholar 

  26. Argote, L., Ingram, P.: Knowledge transfer: a basis for competitive advantage in firms. Organ. Behav. Hum. Decis. Process. 82(1), 150–169 (2000)

    Article  Google Scholar 

  27. Tamer Cavusgil, S., Calantone, R.J., Zhao, Y.: Tacit knowledge transfer and firm innovation capability. J. Bus. Ind. Mark. 18(1), 6–21 (2003)

    Article  Google Scholar 

  28. Bartol, K.M., Srivastava, A.: Encouraging knowledge sharing: the role of organizational reward systems. J. Leadersh. Organ. Studies 9(1), 64–76 (2002)

    Article  Google Scholar 

  29. Gurteen, D.: Creating a knowledge sharing culture. Knowl. Manag. Magaz. 2(5), 1–4 (1999)

    Google Scholar 

  30. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  Google Scholar 

  31. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

Download references

Acknowledgements

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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Authors and Affiliations

  1. Business School, Chengdu University of Technology, Chengdu, 610059, People’s Republic of China

    Li Zhang & Qifeng Wei

  2. Business School, Sichuan University, Chengdu, 610064, People’s Republic of China

    Yuan Yuan

  3. Management School, Sichuan University of Science and Engineering, Zigong, 643000, People’s Republic of China

    Yuxue Li

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  1. Li Zhang
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  2. Qifeng Wei
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  3. Yuan Yuan
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  4. Yuxue Li
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Correspondence to Qifeng Wei.

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Zhang, L., Wei, Q., Yuan, Y. et al. Knowledge diffusion simulation of knowledge networks: based on complex network evolutionary algorithms. Cluster Comput 22 (Suppl 6), 15255–15265 (2019). https://doi.org/10.1007/s10586-018-2559-3

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