Abstract Details
Activity Number:
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568
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 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 #312333
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Title:
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Detecting Overlapping Communities in Networks with Spectral Methods
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Author(s):
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Yuan Zhang*+ and Elizaveta Levina and Ji Zhu
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Keywords:
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networks ;
overlapping communities ;
spectral clustering
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Abstract:
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Community detection is a fundamental problem in network analysis, and while many methods have been proposed to partition networks into disjoint communities, there is a need for more flexible models that allow overlapping communities, which are common in practice. Here we propose a flexible generative model that describes networks with overlapping communities. The overlapping community membership parameters can be efficiently estimated via a spectral clustering type algorithm we propose. We show that the estimation is consistent under mild conditions, and demonstrate the effectiveness of our method in numerical experiments and data applications.
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Authors who are presenting talks have a * after their name.
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