Simultaneously finding fundamental articles and new topics using a community tracking method

Tieyun Qian; Jaideep Srivastava; Zhiyong Peng; Phillip C.Y. Sheu

doi:10.1007/978-3-642-01307-2_82

Simultaneously finding fundamental articles and new topics using a community tracking method

Tieyun Qian, Jaideep Srivastava, Zhiyong Peng, Phillip C.Y. Sheu

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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.

Original language	English (US)
Title of host publication	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages	796-803
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-01307-2_82
State	Published - Jul 23 2009
Event	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand Duration: Apr 27 2009 → Apr 30 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5476 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/Territory	Thailand
City	Bangkok
Period	4/27/09 → 4/30/09

Keywords

Community tracking
Fundamental article finding
New topic identification

Publisher link

10.1007/978-3-642-01307-2_82

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Qian, T., Srivastava, J., Peng, Z., & Sheu, P. C. Y. (2009). Simultaneously finding fundamental articles and new topics using a community tracking method. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 796-803). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI). https://doi.org/10.1007/978-3-642-01307-2_82

Simultaneously finding fundamental articles and new topics using a community tracking method. / Qian, Tieyun; Srivastava, Jaideep; Peng, Zhiyong et al.
13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 796-803 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Qian, T, Srivastava, J, Peng, Z & Sheu, PCY 2009, Simultaneously finding fundamental articles and new topics using a community tracking method. in 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5476 LNAI, pp. 796-803, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, Thailand, 4/27/09. https://doi.org/10.1007/978-3-642-01307-2_82

Qian T, Srivastava J, Peng Z, Sheu PCY. Simultaneously finding fundamental articles and new topics using a community tracking method. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 796-803. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-01307-2_82

Qian, Tieyun ; Srivastava, Jaideep ; Peng, Zhiyong et al. / Simultaneously finding fundamental articles and new topics using a community tracking method. 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. pp. 796-803 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Simultaneously finding fundamental articles and new topics using a community tracking method

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Keywords

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