Identifying online communities of interest using side information

Christopher S. Leberknight, Ali Tajer, Mung Chiang, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This research investigates the potential to identify communities and individuals of interest in a weighted network by incorporating side information corresponding to the prior probability of engaging in a specific activity. A brief review of community detection techniques is presented followed by a discussion of a proposed probabilistic model for identifying communities using seeds with side information. A simulation of the model demonstrates the required parameters to detect individuals in the network who are likely to engage in a specific activity. Results highlight the ability of the model to identify small social communities by accounting for the affinity or strength of the relationships between individuals of interest and other individuals in the network.

Original languageEnglish
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages137-140
Number of pages4
DOIs
StatePublished - 6 Nov 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: 5 Aug 20128 Aug 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Other

Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
CountryUnited States
CityAnn Arbor, MI
Period5/08/128/08/12

Keywords

  • Clustering
  • Community Detection
  • Online Social Networks
  • Viral Marketing

Fingerprint Dive into the research topics of 'Identifying online communities of interest using side information'. Together they form a unique fingerprint.

Cite this