Presentation 2002/7/19
Modeling of Growing Networks with Communities
Masahiro KIMURA, Kazumi SAITO, Naonori UEDA,
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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.
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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
Conference Date 2002/7/19(1days)
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Registration To Neurocomputing (NC)
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
Date of Issue