Generalized Network Generation Method for Small-World Network and Scale-Free Network 


Vol. 41,  No. 7, pp. 754-764, Jul.  2016


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  Abstract

To understand and analyze SNS(Social Network Service) two important classes of networks, small-world and scale-free networks have gained a lot of research interests. In this study, a generalized network generation method is developed, which can produce small-world network, scale-free network, or network with the properties of both small-world and scale-free by controlling two input parameters. By tuning one parameter we can represent the small-world property and by tuning the other one we can represent both scale-free and small-world properties. For the network measures to represent small-world and scale-free properties clustering coefficient, average shortest path distance and power-law property are used. Using the model proposed in this study we can have more clear understanding about relationships between small-world network and scale-free network. Using numerical examples we have verified the effects of two parameters on clustering coefficient, average shortest path distance and power-law property. Through this investigation it can be shown that small-world network, scale-free network or both can be generated by tuning two input parameters properly.

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  Cite this article

[IEEE Style]

K. Lee, J. Lee, H. Choe, "Generalized Network Generation Method for Small-World Network and Scale-Free Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 7, pp. 754-764, 2016. DOI: .

[ACM Style]

Kang-won Lee, Jae-hoon Lee, and Hye-zin Choe. 2016. Generalized Network Generation Method for Small-World Network and Scale-Free Network. The Journal of Korean Institute of Communications and Information Sciences, 41, 7, (2016), 754-764. DOI: .

[KICS Style]

Kang-won Lee, Jae-hoon Lee, Hye-zin Choe, "Generalized Network Generation Method for Small-World Network and Scale-Free Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 7, pp. 754-764, 7. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880