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SUMMARY:Kernels for graph comparison - Karsten Borgwardt\, University of C
 ambridge
DTSTART:20071116T120000Z
DTEND:20071116T130000Z
UID:TALK8795@talks.cam.ac.uk
CONTACT:Johanna Geiss
DESCRIPTION:As new graph structured data is constantly being generated\, l
 earning and data mining on graphs have become a challenge in application a
 reas such as molecular biology\, telecommunications\, chemoinformatics\, a
 nd social network analysis. The central algorithmic problem in these areas
 \, measuring similarity of graphs\, has therefore received extensive atten
 tion in the recent past. Unfortunately\, existing approaches are slow\, la
 cking in expressivity\, or hard to parameterize.\n\nGraph kernels have rec
 ently been proposed as a theoretically sound and promising approach to the
  problem of graph comparison. Their attractivity stems from the fact that 
 by defining a kernel on graphs\, a whole family of data mining and machine
  learning algorithms becomes applicable to graphs.\n\nThese kernels on gra
 phs must respect both the information represented by the topology and the 
 node and edge labels of the graphs\, while being efficient to compute. Exi
 sting methods fall woefully short\; they miss out on important topological
  information\, are plagued by runtime issues\, and do not scale to large g
 raphs.\n\nIn this talk\, we will present our work on speeding up graph ker
 nels and we will describe applications of graph kernels.
LOCATION:SW01 Computer Laboratory
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