Presentation | 2002/7/19 Modeling of Growing Networks with Communities Masahiro KIMURA, Kazumi SAITO, Naonori UEDA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we propose a growing network model and its learning algorithm. Unlike the conventional scale-free models, we incorporate community structure, which is an important characteristic of many real-world networks including the Web. In our experiments, we confirmed that the proposed model exhibits a degree distribution with a power-law tail, and our method can precisely estimate the probability of a new link creation from data without community information. Moreover, by introducing a measure of dynamic hub-degrees, we could predict the change of hub-degrees between communities. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | growing network models / community structure / scale-free models / latent variable models / hub-degrees prediction |
Paper # | NC2002-40 |
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Committee | NC |
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Conference Date | 2002/7/19(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Modeling of Growing Networks with Communities |
Sub Title (in English) | |
Keyword(1) | growing network models |
Keyword(2) | community structure |
Keyword(3) | scale-free models |
Keyword(4) | latent variable models |
Keyword(5) | hub-degrees prediction |
1st Author's Name | Masahiro KIMURA |
1st Author's Affiliation | NTT Communication Science Laboratories() |
2nd Author's Name | Kazumi SAITO |
2nd Author's Affiliation | NTT Communication Science Laboratories |
3rd Author's Name | Naonori UEDA |
3rd Author's Affiliation | NTT Communication Science Laboratories |
Date | 2002/7/19 |
Paper # | NC2002-40 |
Volume (vol) | vol.102 |
Number (no) | 253 |
Page | pp.pp.- |
#Pages | 6 |
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