Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:A4L-A

Session:

Number:126

Classification of real networks by using classical multidimensional scaling

Yong Gao,  Kaori Kuroda,  Yutaka Shimada,  Kantato Fujiwara,  Tohru Ikeguchi,  

pp.126-129

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.126

PDF download (1.7MB)

Summary:
Real systems can be described by networks: Internet, WWW, neural networks, and human relationships. A novel framework which combines the complex network theory and the nonlinear time series analysis becomes one of the useful tools to understand characteristics of these complex networks and reveal hidden structures underlying in these complex networks. In the framework, using the classical multidimensional scaling, complex networks can be transformed into time series. In this paper we investigated the distribution of values of the time series transformed from the networks by the transformation method of the framework. We compared the distributions obtained from real networks with those from networks generated from the Watts-Strogatz model and the Barabási-Albert model to discuss characteristic properties of the real networks.

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