Qian TY, Srivastava J, Peng ZY, Sheu PCY, "Simultaneously finding fundamental articles and new topics using a community tracking method" Advances in Knowledge Discovery and Data Mining, Proceedings, Lecture Notes in AI, p.796-803, 2009

[Eugene Garfield]

TITLE : Simultaneously Finding Fundamental Articles and New Topics Using a 
Community Tracking Method 

Author(s): Qian TY (Qian, Tieyun)1, Srivastava J (Srivastava, Jaideep), 
Peng ZY (Peng, Zhiyong), Sheu PCY (Sheu, Phillip C. Y.)1  
Editor(s): Theeramunkong T; Kijsirikul B; Cercone N; Ho TB  

Source: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS   Book 
Series: Lecture Notes in Artificial Intelligence    Volume: 5476    Pages: 
796-803    Published: 2009    

Times Cited: 0     References: 16     Citation Map      

Conference Information: 13th Pacific-Asia Conference on Knowledge and Data 
Mining
Bangkok, THAILAND, APR 27-30, 2009
Sirindhorn Int Inst Technol; Thammasat Univ; Chulalonkorn Univ; Asian Inst 
Technol; Natl Elect & Comp Technol Ctr; Thailand Convent & Exhibit Bureau; 
AF Off Sci Res, Asian Off Aerosp Res & Dev  

 Abstract: In this paper, we study the relationship between fundamental 
articles and new topics and present a new method to detect recently formed 
topics and its typical articles simultaneously. Based on community 
partition, the proposed method first identifies the emergence of a new 
theme by tracking the change of the community where the top cited nodes 
lie. Next, the paper with a high citation number belonging to this new 
topic is recognized as a fundamental article. Experimental results on real 
dataset show that our method can detect new topics with only a subset of 
data in a timely manner, and the identified papers for these topics are 
found to have a long lifespan and keep receiving citations in the future. 

Document Type: Proceedings Paper  
Language: English  
Author Keywords: Community tracking; Fundamental article finding; New topic 
identification  

Reprint Address: Qian, TY (reprint author), Wuhan Univ, State Key Lab 
Software Engn, 16 Luojiashan Rd, Wuhan 430072, Hubei Peoples R China  
Addresses: 
1. Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Hubei Peoples R 
China  
Publisher: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, 
GERMANY  
IDS Number: BKN07  
ISSN: 0302-9743  
ISBN: 978-3-642-01306-5  
              
CITED REFERENCES:

1.  BLEI DM
Latent Dirichlet allocation 
JOURNAL OF MACHINE LEARNING RESEARCH 3 : 993 2003 
2.  BOLLEN J
INFORM PROCESSING MA 41 : 2005 
3.  BRIN S
WWW7 COMPUTER NETWOR 30 : 107 1998 
4.  CAHLIK T
SCIENTOMETRICS 49 : 2000 
5.  GARFIELD E
CITATION ANALYSIS AS A TOOL IN JOURNAL EVALUATION - JOURNALS CAN BE RANKED 
BY FREQUENCY AND IMPACT OF CITATIONS FOR SCIENCE POLICY STUDIES 
SCIENCE 178 : 471 1972 
6.  GRIFFITHS TL
Finding scientific topics 
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF 
AMERICA 101 : 5228 DOI 10.1073/pnas.0307752101 2004 
7.  KLEINBERG JM
Authoritative sources in a hyperlinked environment 
JOURNAL OF THE ACM 46 : 604 1999 
8.  KUHN TS
STRUCTURE SCI REVOLU : 1970 
9.  LEHMANN S
MEASURES MISMEASURES : 2005 
10.  LEICHT EA
Large-scale structure of time evolving citation networks 
EUROPEAN PHYSICAL JOURNAL B 59 : 75 DOI 10.1140/epjb/e2007-00271-7 2007 
11.  MEI Q
P 11 SIGKDD : 2005 
12.  NEWMAN MEJ
PHYS REV E 59 : 2004 
13.  STEYVERS M
P 10 ACM SIGKDD : 2004 
14.  WANG X
P 12 ACM SIGKDD : 2006 
15.  WASSERMAN S
SOCIAL NETWORK ANAL : 1994 
16.  ZHOU D
P 7 ICDM : 2007