Mathematical Modeling of Complex Networks by Using a Learning Approach
Project/Area Number |
18500113
|
Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
Intelligent informatics
|
Research Institution | Nagoya Institute of Technology |
Principal Investigator |
NAKANO Ryohei Nagoya Institute of Technology, Dept of Computer Science, Professor (90324467)
|
Co-Investigator(Kenkyū-buntansha) |
SAITO Kazumi University of Shizuoka, School of Administration and Informatics, Professor (80379544)
KITAKOSHI Daisuke Nagoya Institute of Technology, Dept of Computer Science, Assistant Professor (50378238)
KIMURA Masahiro Ryukoku University, Dept of Electronics and Informatics, Ryukoku University Dept of Electronics and Informatics (10396153)
|
Project Period (FY) |
2006 – 2007
|
Project Status |
Completed (Fiscal Year 2007)
|
Budget Amount *help |
¥3,260,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥360,000)
Fiscal Year 2007: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2006: ¥1,700,000 (Direct Cost: ¥1,700,000)
|
Keywords | complex network / network growth / information diffusion / mixture of communities / forest fire model / blog-roll / link prediction / 混合コミュティモデル |
Research Abstract |
The aim of this research is to analysis the Internet which keeps growing rapidly as a complex network, to find a mathematical model for the growing network, and to explain its behavior by using the mathematical model. Firstly we proposed a method which extracts communities from social network data. Secondly, after defining an influence maximization problem for information diffusion in a social network, we proposed a very fast method to solve the problem by making good use of bond-percolation. Finally, we proposed a method which estimates probable link generations for network growth. All the proposed methods were evaluated for real large-scale networks.
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Report
(3 results)
Research Products
(24 results)