Scalable community discovery on textual data with relations

Huajing Li, Zaiqing Nie, Wang Chien Lee, C. Lee Giles, Ji Rong Wen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations

Abstract

Every piece of textual data is generated as a method to convey its authors' opinion regarding specific topics. Authors deliberately organize their writings and create links, i.e., references, acknowledgments, for better expression. Thereafter, it is of interest to study texts as well as their relations to understand the underlying topics and communities. Although many efforts exist in the literature in data clustering and topic mining, they are not applicable to community discovery on large document corpus for several reasons. First, few of them consider both textual attributes as well as relations.Second, scalability remains a significant issue for large-scale datasets. Additionally, most algorithms rely on a set of initial parameters that are hard to be captured and tuned. Motivated by the aforementioned observations, a hierarchical community model is proposed in the paper which distinguishes community cores from affiliated members. We present our efforts to develop a scalable community discovery solution for large-scale document corpus. Our proposal tries to quickly identify potential cores as seeds of communities through relation analysis. To eliminate the influence of initial parameters, an innovative attribute-based core merge process is introduced so that the algorithm promises to return consistent communities regardless initial parameters. Experimental results suggest that the proposed method has high scalability to corpus size and feature dimensionality, with more than 15% topical precision improvement compared with popular clustering techniques.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM'08
Pages1203-1212
Number of pages10
DOIs
StatePublished - Dec 1 2008
Event17th ACM Conference on Information and Knowledge Management, CIKM'08 - Napa Valley, CA, United States
Duration: Oct 26 2008Oct 30 2008

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other17th ACM Conference on Information and Knowledge Management, CIKM'08
CountryUnited States
CityNapa Valley, CA
Period10/26/0810/30/08

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Scalable community discovery on textual data with relations'. Together they form a unique fingerprint.

Cite this