Modeling and robustness of knowledge network in supply chain

Abstract

The growth and evolution of the knowledge network in supply chain can be characterized by dynamic growth clustering and non-homogeneous degree distribution. The networks with the above characteristics are also known as scale-free networks. In this paper, the knowledge network model in supply chain is established, in which the preferential attachment mechanism based on the node strength is adopted to simulate the growth and evolution of the network. The nodes in the network have a certain preference in the choice of a knowledge partner. On the basis of the network model, the robustness of the three network models based on different preferential attachment strategies is investigated. The robustness is also referred to as tolerances when the nodes are subjected to random destruction and malicious damage. The simulation results of this study show that the improved network has higher connectivity and stability.

Keywords

knowledge network / preferential attachment / modeling / robustness

References

[1] Wasserman S, Faust K. Social Network Analysis[M]. 1994, Cambridge: Cambridge University Press.

[2] Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the internet topology[J]. ACM SIGCOMM Computer Communication Review, 1999, 29(4): 251-262.

[3] Albert R, Jeong H, Barabasi A L. Error and attack tolerance of complex networks[J]. Nature, 2000, 406(6794): 378-382.

[4] Jeong H, Tombor B, Albert R, et al. The large-scale organization of metabolic networks[J]. Nature, 2000, 407(6804): 651-654.

[5] Newman M E J. The structure and function of complex networks[J]. SIAM Rev, 2003, 45(2): 167-256.

[6] Albert R, Barabasi A L. Statistical mechanics of complex networks[J]. Rev Mod Phys, 2002, 74(1): 47-97.

[7] Olin J G, Geris P, Kasarda J D, et al. Knowledge management across multi-tier enterprises: The promise of intelligent software in the auto industry[J]. European Management Journal, 1999, 17(4): 335-347.

[8] Costa L F, Oliveira O N Jr Travieso G, et al. Analyzing and modeling real-world phenomena with complex networks: A survey of applications[J]. Advances in Physics, 2011, 60(3): 329-412.

[9] Xi Y, Dang Yanzhong. The methods of expert’s domain knowledge discovery and representation based on the knowledge network[J]. Systems Engineering, 2005, 23(8): 110-115.

[10] Crucitti P, Latora V, Marchiori M, et al. Error and attack tolerance of complex networks[J]. Physica A: Statistical Mechanics and Its Applications, 2004, 340(1–3): 388-394.

[11] Wagner H H, Murphy M A, Holderegger R, et al. Developing an interdisciplinary, distributed graduate course for twenty-first century scientists[J]. Bioscience, 2012, 62(2): 182-188.

[12] Jeong H, Neda Z, Barabasi A L. Measuring preferential attachment for evolving networks[J]. Europhysics Letters, 2003, 61(4): 567-572.

[13] Chaloner D T, Wotton R S. Overview: The links that bind aquatic ecosystems[J]. Journal of the North American Benthological Society, 2011, 30(3): 751-761.