BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Latent space models for multiplex networks with shared structure -
  Elizaveta Levina (University of Michigan)
DTSTART:20221028T130000Z
DTEND:20221028T140000Z
UID:TALK182723@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:Statistical tools for analysis of a single network are now wid
 ely available\, but many practical settings involve multiple networks.  Th
 ese can arise as a sample of networks (for example\, brain connectivity ne
 tworks for a sample of patients)\, a single network with multiple types of
  edges (for example\, trade between countries in many different commoditie
 s)\, or a single network evolving over time.  The term multiplex networks 
 refers to multiple and generally heterogeneous networks observed on the sa
 me shared node set\; the two examples above are both multiplex networks.  
  We propose a new latent space model for multiplex networks which answers 
 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 this 
 from data and pools information adaptively.  We establish identifiability\
 , develop a fitting procedure using convex optimization in combination wit
 h a nuclear norm penalty\, and prove a guarantee of recovery for the laten
 t positions as long as there is sufficient separation between the shared a
 nd the individual latent subspaces.   We compare the model to competing me
 thods in the literature on simulated networks and on a multiplex network d
 escribing the worldwide trade of agricultural products.
LOCATION:MR12\, Centre for Mathematical Sciences
END:VEVENT
END:VCALENDAR