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SUMMARY:Graph Data Compression: Practical Methods and Information-Theoreti
 c Limits - Prof. Justin Coon\, University of Oxford
DTSTART:20250507T130000Z
DTEND:20250507T140000Z
UID:TALK225952@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:Many modern datasets possess complex correlation structures.  
 Such data is typically stored as graphs.  Examples of graph data include s
 ocial networks\, web graphs\, biological networks\, and neural networks.  
 These graph datasets often contain hundreds of millions of nodes and billi
 ons of edges\, which leads to a significant problem in terms of storage an
 d processing.  Therefore\, there is need to compress graphs and store them
  efficiently without losing much information.  In this talk\, I will give 
 an introduction to the developing field of graph compression.  I will disc
 uss the basic problems encountered in practice and some of the solutions t
 hat have been proposed.  I will also present a few results detailing infor
 mation theoretic limits on compressing graphs. 
LOCATION:MR5\, CMS Pavilion A
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