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CATEGORIES:Statistics
SUMMARY:Latent space models for multiplex networks with sh
 ared structure - Elizaveta Levina (University of M
 ichigan)
DTSTART;TZID=Europe/London:20221028T140000
DTEND;TZID=Europe/London:20221028T150000
UID:TALK182723AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/182723
DESCRIPTION:Statistical tools for analysis of a single network
  are now widely available\, but many practical set
 tings involve multiple networks.  These can arise 
 as a sample of networks (for example\, brain conne
 ctivity networks for a sample of patients)\, a sin
 gle network with multiple types of edges (for exam
 ple\, trade between countries in many different co
 mmodities)\, or a single network evolving over tim
 e.  The term multiplex networks refers to multiple
  and generally heterogeneous networks observed on 
 the same shared node set\; the two examples above 
 are both multiplex networks.   We propose a new la
 tent space model for multiplex networks which answ
 ers a key question:   what part of the underlying 
 structure is shared between all the networks\, and
  what is unique to each one?  Our model learns thi
 s from data and pools information adaptively.  We 
 establish identifiability\, develop a fitting proc
 edure using convex optimization in combination wit
 h a nuclear norm penalty\, and prove a guarantee o
 f recovery for the latent positions as long as the
 re is sufficient separation between the shared and
  the individual latent subspaces.   We compare the
  model to competing methods in the literature on s
 imulated networks and on a multiplex network descr
 ibing the worldwide trade of agricultural products
 .
LOCATION:MR12\, Centre for Mathematical Sciences
CONTACT:Qingyuan Zhao
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