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SUMMARY:Progress on the connection between spectral embedding and network 
 models used by the probability\, statistics and machine-learning communiti
 es - Patrick Rubin-Delanchy (University of Bristol)
DTSTART:20180301T110000Z
DTEND:20180301T120000Z
UID:TALK101920@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:In this talk\, I give theoretical and methodological results\,
  based on work spanning Johns Hopkins\, the Heilbronn Institute for Mathem
 atical Research\, Imperial and Bristol\, regarding the connection between 
 various graph spectral methods and commonly used network models which are 
 popular in the probability\, statistics and machine-learning communities. 
 An attractive feature of the results is that they lead to very simple take
 -home messages for network data analysis: a) when using spectral embedding
 \, consider eigenvectors from both ends of the spectrum\; b) when implemen
 ting spectral clustering\, use Gaussian mixture models\, not k-means\; c) 
 when interpreting spectral embedding\, think of "mixtures of behaviour" ra
 ther than "distance". Results are illustrated with cyber-security applicat
 ions.  <br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
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