Presentation 2001/7/12
An Approach to Find Related Communities Based on Bipartite Graphs
P.Krishna Reddy, Masaru Kitsuregawa,
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Abstract(in English) In this paper we investigate the problem of extracting related community information from a large collection of Web-pages by performing hyperlink analysis. We consider a group of communities(say related community set)related if they have common interests on some topic. In the proposed approach, we employ dense bipartite graph(DBG)abstraction for two purposes. From the given page collection, we first extract all the communities by mathematically abstracting the community as a DBG over a set of pages. Next, our approach extracts related communities among the communities by abstracting related community set as a DBG over set of communities. We report experimental results on 10 GB TREC(Text REtrieval Conference)data collection that contains 1.7 million pages and 21.5 million links. The results demonstrate that the proposed approach extracts related community structures.
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Keyword(in English) Web mining / Communities / trawling / Link analysis
Paper # DE2001-79
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Committee DE
Conference Date 2001/7/12(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Approach to Find Related Communities Based on Bipartite Graphs
Sub Title (in English)
Keyword(1) Web mining
Keyword(2) Communities
Keyword(3) trawling
Keyword(4) Link analysis
1st Author's Name P.Krishna Reddy
1st Author's Affiliation Institute of Industrial Science, The University of Tokyo()
2nd Author's Name Masaru Kitsuregawa
2nd Author's Affiliation Institute of Industrial Science, The University of Tokyo
Date 2001/7/12
Paper # DE2001-79
Volume (vol) vol.101
Number (no) 193
Page pp.pp.-
#Pages 8
Date of Issue