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SUMMARY:Clustering Sparse Graphs - Sanghavi\, S (University of Texas at Au
 stin)
DTSTART:20130814T090000Z
DTEND:20130814T094500Z
UID:TALK46640@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Graph clustering involves the task of partitioning nodes\, so 
 that the edge density is higher within partitions as opposed to across par
 titions. A natural problem\, it represents a first step in a wide array of
  applications in network analysis\, community detection\, recommendation s
 ystems etc.\n\nA classic and popular statistical setting for evaluating be
 tween different solutions to this problem is the stochastic block model\, 
 also referred to as the planted partition model. In this talk\, we present
  a new algorithm for this problem\, which improves by polynomial factors o
 ver the performance of all previous known algorithms. It is based on conve
 x optimization\, and draws a connection between this problem and a differe
 nt field: high-dimensional statistical inference.\n
LOCATION:Seminar Room 1\, Newton Institute
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