Presentation 2013-03-15
Analysis on real networks by classical multidimensional scaling
Yong GAO, Kaoli KURODA, Yutaka SHIMADA, Kantaro FUJIWARA, Tohru IKEGUCHI,
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Abstract(in English) In the real world, we have a wide variety of complex networks, such as Internet, neural networks, human relationships and so on. To understand characteristics and structure of these complex networks, a new framework of combining the complex network theory and nonlinear time series analysis has been proposed. One of the frameworks uses the classical multidimensional scaling to transform complex networks to time series. In this report, by using this transforming method, we investigated the distribution of coordinate values of the time series transformed from the networks. We compared the distribution of coordinate values of real networks with that of network models such as the Watts-Strogatz model and the Barabasi-Albert model.
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Keyword(in English) complex network / nonlinear time series analysis / classical multidimensional scaling
Paper # NLP2012-162
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Committee NLP
Conference Date 2013/3/7(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Analysis on real networks by classical multidimensional scaling
Sub Title (in English)
Keyword(1) complex network
Keyword(2) nonlinear time series analysis
Keyword(3) classical multidimensional scaling
1st Author's Name Yong GAO
1st Author's Affiliation Graduate School of Science and Engineering, Saitama University()
2nd Author's Name Kaoli KURODA
2nd Author's Affiliation Graduate School of Science and Engineering, Saitama University
3rd Author's Name Yutaka SHIMADA
3rd Author's Affiliation FIRST, Aihara Innovative Mathematical Modelling Project, JST:Institute of Industrial Science, the University of Tokyo
4th Author's Name Kantaro FUJIWARA
4th Author's Affiliation Graduate School of Science and Engineering, Saitama University
5th Author's Name Tohru IKEGUCHI
5th Author's Affiliation Graduate School of Science and Engineering, Saitama University:Saitama University Brain Science Insititute
Date 2013-03-15
Paper # NLP2012-162
Volume (vol) vol.112
Number (no) 487
Page pp.pp.-
#Pages 6
Date of Issue