By Topic

Sampling Online Social Networks Using Coupling from the Past

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kenton White ; Girih, Ottawa, ON, Canada ; Guichong Li ; Nathalie Japkowicz

Recent research has focused on sampling online social networks (OSNs) using traditional Markov Chain Monte Carlo (MCMC) techniques such as the Metropolis-Hastings algorithm (MH). While these methods have exhibited some success, the techniques suffer from slow mixing rates by themselves, and the resulting sample is usually approximate. An appealing solution is to apply the state of the art MCMC technique, Coupling From The Past (CFTP), for perfect sampling of OSNs. In this initial research, we explore theoretical and methodological issues such as customizing the update function and generating small sets of non-trivial states to adapt CFTP for sampling OSNs. Our research proposes the possibility of achieving perfect samples from large and complex OSNs using CFTP.

Published in:

2012 IEEE 12th International Conference on Data Mining Workshops

Date of Conference:

10-10 Dec. 2012