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

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages796-803
Number of pages8
DOIs
StatePublished - Jul 23 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: Apr 27 2009Apr 30 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
CountryThailand
CityBangkok
Period4/27/094/30/09

Keywords

  • Community tracking
  • Fundamental article finding
  • New topic identification

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