This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 248
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #308798
Title: Extract Communities from Networks
Author(s): Yunpeng Zhao*+ and Elizaveta Levina and Ji Zhu
Companies: University of Michigan and University of Michigan and University of Michigan
Address: 439 West Hall , Ann Arbor, MI, 48109,
Keywords: Block model ; Community identification ; Network clustering
Abstract:

Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields. Most of the existing community detection methods focus on partitioning the network into cohesive communities, with the expectation of many links between the members of the same community and few links between different communities. However, many real networks contain, in addition to communities, a number of sparsely connected nodes that are best classified as background. We propose a new criterion for community extraction, which aims to separate tightly linked communities from a sparsely connected background, extracting one community at a time. The new criterion is shown to perform well in simulation studies and on studies and on several real networks. We also establish asymptotic consistency of the proposed method under the block model assumption.


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