Presentation 2004/3/9
A Kernel-based Account of Bibliometric Measures
Takahiko ITO, Masashi SHINBO, Taku KUDO, Yuji MATSUMOTO,
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Abstract(in English) We explore the application of kernel methods to citation analysis. We show that a family of kernels on graphs provides a unified perspective on the three bibliometric measures that have been discussed independently: relatedness between documents, overall importance of individual documents, and importance of documents relative to one or more (root) documents (relative importance). The framework provided by the kernels establishes relative importance as an intermediate between relatedness and overall importance, in which the degree of 'relativity,' or the bias between relatedness and importance, is naturally controlled by a parameter characterizing the kernel family.
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Keyword(in English) Kernel Methods / Link Analysis / Citation Analysis / Bibliometrics
Paper # AI2003-96
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Committee AI
Conference Date 2004/3/9(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) A Kernel-based Account of Bibliometric Measures
Sub Title (in English)
Keyword(1) Kernel Methods
Keyword(2) Link Analysis
Keyword(3) Citation Analysis
Keyword(4) Bibliometrics
1st Author's Name Takahiko ITO
1st Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology()
2nd Author's Name Masashi SHINBO
2nd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
3rd Author's Name Taku KUDO
3rd Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
4th Author's Name Yuji MATSUMOTO
4th Author's Affiliation Graduate School of Information Science, Nara Institute of Science and Technology
Date 2004/3/9
Paper # AI2003-96
Volume (vol) vol.103
Number (no) 725
Page pp.pp.-
#Pages 6
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