A design method for the complex network growth model
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- @InProceedings{Mizuno:2013:SICE,
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author = "Haruki Mizuno and Takashi Okamoto and
Seiichi Koakutsu and Hironori Hirata",
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title = "A design method for the complex network growth model",
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booktitle = "Proceedings of SICE Annual Conference (SICE 2013)",
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year = "2013",
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month = "14-17 " # sep,
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pages = "571--576",
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keywords = "genetic algorithms, genetic programming, Complex
Network, Network Growth Model, Network Design",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6736203",
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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.",
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notes = "Also known as \cite{6736203}",
- }
Genetic Programming entries for
Haruki Mizuno
Takashi Okamoto
Seiichi Koakutsu
Hironori Hirata
Citations