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

Authors:
Tieyun Qian;Jaideep Srivastava;Zhiyong Peng;Phillip C. Sheu
Affiliations:
State Key Lab of Software Engineering, Wuhan Uviersity, Wuhan, Hubei, China 430072;Dept of Computer Science & Engineering, University of Minnesota, Twin Cities,;Computer School, Wuhan University,;State Key Lab of Software Engineering, Wuhan Uviersity, Wuhan, Hubei, China 430072 and Department of Electrical and Computer Engineering, University of California, Irvine
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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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.