This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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532
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #306760 |
Title:
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An Example of Performance Analysis for Network Community Detection
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Author(s):
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Luke Fostvedt*+ and Dan Nordman and Alyson Wilson
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Companies:
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Iowa State University and Iowa State University and Iowa State University
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Address:
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1205 Snedecor Hall, Ames, IA, 50011,
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Keywords:
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Community Detection ;
Erdos Renyi ;
Random Graph ;
Scale-Free
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Abstract:
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Community detection algorithms have many applications ranging from search engines on the world wide web to the detection terrorist networks. While the computer scientists are trying to detect "clumpiness" in networks, as statisticians we are analyzing the performance of the algorithm. We pick a node and determine its expected number of neighbors with a degree greater than or equal to this chosen node. This is colloquially referred to the number of nodes in the chosen node's "bucket". We show that, for a multigraph with an arbitrary node degree distribution, both the number of nodes in the bucket and the number of pairs of nodes in the bucket are asymptotically finite. This is in contrast to an Erdos Renyi random graph where this quantity increases with O(n).
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