BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Bastian Rieck (Munich)
DTSTART:20230426T090000Z
DTEND:20230426T100000Z
DTSTAMP:20260423T021035Z
UID:CompAlg/14
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/14/"
 >Curvature for Graph Learning</a>\nby Bastian Rieck (Munich) as part of Ma
 chine Learning Seminar\n\n\nAbstract\nCurvature bridges geometry and topol
 ogy\, using local information to derive global statements. While well-know
 n in a differential topology context\, it was recently extended to the dom
 ain of graphs. In fact\, graphs give rise to various notions of curvature\
 , which differ in expressive power and purpose. We will give a brief overv
 iew of curvature in graphs\, define some relevant concepts\, and show thei
 r utility for data science and machine learning applications. In particula
 r\, we shall discuss two applications: first\, the use of curvature to dis
 tinguish between different models for synthesising new graphs from some un
 known distribution\; second\, a novel framework for defining curvature for
  hypergraphs\, whose structural properties require a more generic setting.
  We will also describe new applications that are specifically geared towar
 ds a treatment by curvature\, thus underlining the utility of this concept
  for data science.\n
LOCATION:https://researchseminars.org/talk/CompAlg/14/
END:VEVENT
END:VCALENDAR