Effective clustering of dense and concentrated online communities

Phan Nhat Hai, Hyoseop Shin

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

3 Scopus citations

Abstract

Most clustering algorithms tend to separate large scale online communities into several meaningful subcommunities by extracting cut points and cut edges. However, these algorithms are not effective on dense and concentrated graphs which do not have any meaningful cut points. Common problems with the previous algorithms are as follows. First, the size of the first cluster is too large as it may contain many incompatible users. Second, the quality and the purity of the clusters are very low. Third, only the dominant first cluster is found to be meaningful. To address these problems, we first propose a graph transformation to separate large scale online communities into two different types of meaningful subgraphs. The first subgraph is the intimacy graph and the second is the reputation graph. Then, we present the effective algorithms for discovering good sub-communities and for excluding incompatible users in these subgraphs. The experimental results show that our algorithms allow for extracting more suitable and meaningful sub-communities than the previous work in dense online networks.

Original languageEnglish (US)
Title of host publicationAdvances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010
Pages133-139
Number of pages7
DOIs
StatePublished - 2010
Externally publishedYes
Event12th International Asia Pacific Web Conference, APWeb 2010 - Busan, Korea, Republic of
Duration: Apr 6 2010Apr 8 2010

Publication series

NameAdvances in Web Technologies and Applications - Proceedings of the 12th Asia-Pacific Web Conference, APWeb 2010

Other

Other12th International Asia Pacific Web Conference, APWeb 2010
CountryKorea, Republic of
CityBusan
Period4/6/104/8/10

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Component
  • Dense community
  • Effective clustering
  • Intimacy community
  • Reputation community
  • Social network

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