A design method for the complex network growth model

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@InProceedings{Mizuno:2013:SICE,
  author =       "Haruki Mizuno and Takashi Okamoto and 
                 Seiichi Koakutsu and Hironori Hirata",
  title =        "A design method for the complex network growth model",
  booktitle =    "Proceedings of SICE Annual Conference (SICE 2013)",
  year =         "2013",
  month =        "14-17 " # sep,
  pages =        "571--576",
  keywords =     "genetic algorithms, genetic programming, Complex
                 Network, Network Growth Model, Network Design",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6736203",
  abstract =     "Many systems that can be modelled using network
                 structures appear in various fields such as
                 informatics, social science, economics, ecology,
                 biology, and engineering. If these systems can be
                 modelled as complex network systems, the complex
                 network design method that finds a desired network
                 structure can become one of strong tools in large-scale
                 system designs. Conventional complex network design
                 methods can only generate a topology of desired
                 network. They can not present the network growth rule.
                 If a network growth model which contains a network
                 growth rule is obtained, then the designer can obtain
                 not only the topology of the desired network but also a
                 guideline for designing desired network. In this study,
                 we propose a complex network growth model design
                 method. In the proposed method, the complex network
                 growth model is obtained by two methods. One is the
                 weighted function optimisation method with the PSO. The
                 weighted function consists of feature quantities. The
                 other is the direct growth model design method with the
                 GP. The growth model is optimised with respect to
                 feature quantities. We try to generate a network growth
                 model which resembles the well-known BA model on the
                 clustering coefficient. We confirm the effectiveness of
                 the proposed method through numerical experiments.",
  notes =        "Also known as \cite{6736203}",
}

Genetic Programming entries for Haruki Mizuno Takashi Okamoto Seiichi Koakutsu Hironori Hirata

Citations