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SUMMARY:Matrix completion in network analysis - Elizaveta Levina (Universi
 ty of Michigan)
DTSTART:20180627T084500Z
DTEND:20180627T093000Z
UID:TALK107428@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Matrix completion is an active area of research in itself\, an
 d a natural tool to apply to network data\, since many real networks are o
 bserved incompletely and/or with noise.   However\, developing matrix comp
 letion algorithms for networks requires taking into account the network st
 ructure.    This talk will discuss three examples of matrix completion use
 d for network tasks.   First\, we discuss the use of matrix completion for
  cross-validation on network data\, a long-standing problem in network ana
 lysis.   Two other examples focus on reconstructing incompletely observed 
 networks\, with structured missingness resulting from network sampling mec
 hanisms.    One scenario we consider is egocentric sampling\, where a set 
 of nodes is selected first and then their connections to the entire networ
 k are observed.    Another scenario focuses on data from surveys\, where p
 eople are asked to name a given number of friends.    We show that matrix 
 completion\, when used appropriately\, can generally be very helpful in so
 lving network problems.
LOCATION:Seminar Room 1\, Newton Institute
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