Community detection with edge content in social media networks

Guo Jun Qi, Charu C. Aggarwal, Thomas Huang

Research output: Contribution to journalConference articlepeer-review

Abstract

The problem of community detection in social media has been widely studied in the social networking community in the context of the structure of the underlying graphs. Most community detection algorithms use the links between the nodes in order to determine the dense regions in the graph. These dense regions are the communities of social media in the graph. Such methods are typically based purely on the linkage structure of the underlying social media network. However, in many recent applications, edge content is available in order to provide better supervision to the community detection process. Many natural representations of edges in social interactions such as shared images and videos, user tags and comments are naturally associated with content on the edges. While some work has been done on utilizing node content for community detection, the presence of edge content presents unprecedented opportunities and flexibility for the community detection process. We will show that such edge content can be leveraged in order to greatly improve the effectiveness of the community detection process in social media networks. We present experimental results illustrating the effectiveness of our approach.

Original languageEnglish (US)
Article number6228112
Pages (from-to)534-545
Number of pages12
JournalProceedings - International Conference on Data Engineering
DOIs
StatePublished - 2012
EventIEEE 28th International Conference on Data Engineering, ICDE 2012 - Arlington, VA, United States
Duration: Apr 1 2012Apr 5 2012

Keywords

  • ignore

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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