Using complex network features for fast clustering in the web

Jintao Tang, Ting Wang, Ji Wang, Qin Lu, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference Companion on World Wide Web, WWW 2011
Pages133-134
Number of pages2
DOIs
Publication statusPublished - 29 Apr 2011
Event20th International Conference Companion on World Wide Web, WWW 2011 - Hyderabad, India
Duration: 28 Mar 20111 Apr 2011

Conference

Conference20th International Conference Companion on World Wide Web, WWW 2011
CountryIndia
CityHyderabad
Period28/03/111/04/11

Keywords

  • complex networks
  • graph clustering
  • parameter estimation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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