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SUMMARY:Measuring rich clubs on weighted networks: definitions and random 
 controls - Jeff Alstott\, Department of Psychiatry\, University of Cambrid
 ge
DTSTART:20130423T100000Z
DTEND:20130423T110000Z
UID:TALK44449@talks.cam.ac.uk
CONTACT:Mikail Rubinov
DESCRIPTION:Networks have a rich club organization when highly-connected n
 odes preferentially connect to other highly-connected nodes. Rich club org
 anization is present in a variety of systems\, including transportation ne
 tworks\, scientific collaboration\, and the brain. Rich clubs can increase
  network efficiency by serving as a high-throughput backbone for signal tr
 afficking. For networks with unweighted links\, rich clubs are measured wi
 th a well-defined metric. For weighted networks\, multiple possible metric
 s exist which each capture different features of the rich club. We describ
 e an integrated framework for measuring unweighted and weighted rich clubs
  that resolves the many different metrics into a single method. This metho
 d focuses on the role of random controls\, particularly on selecting what 
 network features to control for. Using this framework\, we show how previo
 usly reported weighted rich clubs can be biased by inappropriate controls.
  We also identify that rich club organization of links and weights may be 
 separate\, and introduce the method of controlling for link structure and 
 measuring solely the allocation of weight. We use this method to describe 
 the rich club organization of fully connected networks\, including the sto
 ck market and human brain activity.
LOCATION:BCNI seminar room\, Sir William Hardy Building\, Downing Site
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