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UID:20250918T140941EDT-5267c3fsjL@132.216.98.100
DTSTAMP:20250918T180941Z
DESCRIPTION:Title: The Fourier transform on Convergent Graph Sequences\n	\n	A
 bstract:\n	A graph signal is a function on the vertices of a graph. Example
 s of graph signals are: viral load of patients connected through a contact
  network\, or fMRI signals on brain regions linked by functional or physic
 al connections. The graph Fourier transform is a projection of the signal 
 onto the eigenbasis of the adjacency matrix the graph. The matrix is refer
 red to as the shift operator and represents the time evolution of a signal
  over a graph.\n\nSequences of graphs that have similar structure\, in ter
 ms of homomorphism densities\, converge to a {\sl graphon}. A graphon is a
  symmetric\, measurable function on $[0\,1]^2$ which can be interpreted as
  a random graph model. Graphs in a graph sequence converging to a graphon 
 have structure typical of samples from this model. I will present a common
  framework\, derived from the graphon\, to define the Fourier transform of
  such sampled graphs. We recently showed that this approach has the desire
 d convergence properties. I will also discuss the special case of graphs s
 ampled from a {\sl Cayley graphon}. Such graphs are stochastic versions of
  Cayley graphs. The representations of the underlying group allow us to de
 fine a basis for the graphon Fourier transform.\n	\n	This is joint work with
  Mahya Ghandehari and Nauzer Kalyaniwalla.\n\n \n
DTSTART:20221206T180000Z
DTEND:20221206T190000Z
LOCATION:Room 1214\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Jeannette Janssen (Dalhousie University)
URL:/mathstat/channels/event/jeannette-janssen-dalhous
 ie-university-344079
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