Nakamura, Y; Horiike, T; Taira, Y; Sakamoto, H. 2010. An Improved Algorithm for Extracting Research Communities from Bibliographic Data. DATABASE SYSTEMS FOR ADVANCED APPLICATIONS 6193: 338-345

[Eugene Garfield]

Nakamura, Y; Horiike, T; Taira, Y; Sakamoto, H. 2010. An Improved Algorithm 
for Extracting Research Communities from Bibliographic Data. DATABASE 
SYSTEMS FOR ADVANCED APPLICATIONS 6193: 338-345. edited by Yoshikawa, 
M; Meng, XF; Yumoto, T; Ma, Q; Sun, L; Watanabe, C.presented at 15th 
International Conference on DASFAA 2010 in Tsukuba, JAPAN, APR 01-04, 
2010.

Author Full Name(s): Nakamura, Yushi; Horiike, Toshihiko; Taira, Yoshimasa; 
Sakamoto, Hiroshi
Book series title: Lecture Notes in Computer Science
Language: English
Document Type: Proceedings Paper
KeyWords Plus: WEB; FLOW

Abstract: In this paper we improve the performance of the community 
extraction algorithm in [1] from bibliographic data, which was originally 
proposed for web community discovery by [2]. A web community is considered 
to be a set of web pages holding a common topic, in other words, it is a dense 
subgraph induced in web graph. Such subgraphs obtained by the max-flow 
algorithm are called max-flow communities, and this algorithm was improved to 
obtain research communities from bibliographic data by the strategy for 
selection of community nodes in [1]. We propose an improvement of this 
algorithm by carefully selecting initial seed node, and show the performance of 
this algorithm by experiments for the list of many keywords frequently 
appearing in data.

Addresses: [Nakamura, Yushi; Horiike, Toshihiko; Taira, Yoshimasa; Sakamoto, 
Hiroshi] Kyushu Inst Technol, Iizuka, Fukuoka 8208502, Japan
Reprint Address: Nakamura, Y, Kyushu Inst Technol, 680-4 Kawazu, Iizuka, 
Fukuoka 8208502, Japan.

E-mail Address: y_nakamura at donald.ai.kyutech.ac.jp; 
t_horiike at donald.ai.kyutech.ac.jp; taira at donald.ai.kyutech.ac.jp; 
hiroshi at donald.ai.kyutech.ac.jp
ISSN: 0302-9743
ISBN: 978-3-642-14588-9
fulltext: http://www.springerlink.com/content/v48v581r7r513102/