Mining user communities from online social network services

Zhongfeng Zhang, Qiudan Li, Daniel Zeng, Jiesi Cheng

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Online social network services (SNS) have been experiencing rapid growth in recent years. Discovering densely connected user communities from social networks has become a major challenge, with important implications in understanding the structural properties of SNS and improving user-oriented services. Previous work on community discovery has treated user friendship networks and user-generated contents separately. We hypothesize that these two types of information can be fruitfully integrated and propose a unified framework for this task by combining the author-topic model with user friendship network analysis. We empirically show that our approach is capable of discovering interesting user communities using two real-world datasets.

Original languageEnglish (US)
StatePublished - 2010
Event20th Annual Workshop on Information Technologies and Systems, WITS 2010 - St. Louis, MO, United States
Duration: Dec 11 2010Dec 12 2010

Other

Other20th Annual Workshop on Information Technologies and Systems, WITS 2010
CountryUnited States
CitySt. Louis, MO
Period12/11/1012/12/10

Keywords

  • Author topic model
  • Community
  • Multi-relational network
  • Non-negative matrix factorization
  • Social network analysis

ASJC Scopus subject areas

  • Information Systems

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