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SUMMARY:Community detection algorithms: a comparative analysis - Santo For
 tunato (Institute for Scientific Interchange\, Italy)
DTSTART:20100120T160000Z
DTEND:20100120T170000Z
UID:TALK21895@talks.cam.ac.uk
CONTACT:Eiko Yoneki
DESCRIPTION:Uncovering the community structure exhibited by real networks 
 is a\ncrucial step towards an understanding\nof complex systems that goes 
 beyond the local organization of their\nconstituents. Many algorithms have
 \nbeen proposed so far\, but none of them has been subjected to strict\nte
 sts to evaluate their performance.\nHere we test several methods against a
  recently introduced class of\nrealistic benchmark graphs\, with heterogen
 eous distributions of\ndegree and community size. The methods are also tes
 ted against the\nbenchmark by Girvan and Newman\nand on random graphs. As 
 a result of our analysis\, three recent\nalgorithms introduced by Rosvall 
 and Bergstrom\,\nBlondel et al. and Ronhovde and Nussinov\, respectively\,
  have an\nexcellent performance\, with the additional\nadvantage of low co
 mputational complexity\, which enables one to\nanalyze large systems.\n\nB
 io: I got my University degree at the university of Catania (Italy) in 199
 5 and my PhD \nin Germany\, Physics Department of the University of Bielef
 eld\, in 2000. After 4 further years \nin Bielefeld as a postdoctoral rese
 arch associate\, I changed field (moving to complex systems\nfrom statisti
 cal filed theory) and joined Alessandro Vespignani's group at the School o
 f Informatics\nof Indiana University where I stayed as a postdoc from 2005
  till 2007. Since 2007 I am research\nscientist at the Institute for Scien
 tific Interchange of Torino\, where a few months ago I have become\nleader
  of my own research group\, called Sociophysics Lab. \n
LOCATION:FW26\, Computer Laboratory\, William Gates Builiding
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