Abstract Details
Activity Number:
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209
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Type:
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Invited
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Date/Time:
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Monday, August 5, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #307307 |
Title:
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On Consistency of Community Detection in Networks
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Author(s):
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Yunpeng Zhao and Liza Levina and Ji Zhu*+
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Companies:
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George Mason University and University of Michigan and University of Michigan
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Keywords:
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Block model ;
Community detection ;
Consistency ;
Degree-corrected block model
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Abstract:
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Community detection is a fundamental problem in network analysis, with applications in many diverse areas. The stochastic block model is a common tool for model-based community detection, and asymptotic tools for checking consistency of community detection under the block model have been recently developed (Bickel and Chen, 2009). However, the block model is limited by its assumption that all nodes within a community are stochastically equivalent, and provides a poor fit to networks with hubs or highly varying node degrees within communities, which are common in practice. The degree-corrected block model (Karrer and Newman, 2010) was proposed to address this shortcoming, and allows variation in node degrees within a community while preserving the overall block model community structure. In this paper, we establish general theory for checking consistency of community detection under the degree-corrected block model, and compare several community detection criteria under both the standard and the degree-corrected block models. We show which criteria are consistent under which models and constraints, as well as compare their relative performance in practice.
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Authors who are presenting talks have a * after their name.
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