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VERSION:2.0
PRODID:-//RLASKEY//CALENDEROUS//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260915T200533Z
LAST-MODIFIED:20160708T201726Z
DTSTART:20160711T171500Z
DTEND:20160711T181500Z
UID:event1624@bu.edu
URL:http://physics.bu.edu/internal/events/show/1624
SUMMARY:Reading networks – content extraction from complex networks
DESCRIPTION:Featuring Yoram Louzoun\, Lev Muchnik\, Yonatan Rosen\, Bar-Ila
	n University\, Ramat- Gan Israel\n\nPart of the Biophysics/Condensed Matter
	 Seminar Series.\n\nMany real world networks contain signatures of the node
	s function. However\, the same\nnetworks contain a myriad of extra edges\, 
	which hide this inherent signature. I will discuss the\ndefinition of such 
	a signature and propose multiple supervised and unsupervised methods to\nde
	tect nodes with similar function.\nThe most prevalent unsupervised approach
	 for node classification relies on\ndecomposition of a network into communi
	ties. A fundamental assumption underlies community\ndetection is that nodes
	 of identical function are located in the same dense sub-network.\nHowever\
	, nodes with similar classifications may have similar connection patterns i
	n the network\,\neven if they reside in remote regions of the network. We i
	ntroduce a novel method for the\ndetection of groups of non-adjacent nodes 
	with similar function in networks through the\nsimilarity of measures on th
	e network surrounding them. When tested in four real world\nnetworks with g
	round truth classifications\, the groups detected by our algorithm were\nsi
	gnificantly more homogenous than those found by common community detection 
	algorithms.\nWhen used in a supervised context\, precise predictions of ver
	tices function can be accomplished.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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