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SUMMARY:Random walk models of networks: modeling and inferring complex dep
 endence - Benjamin Bloem-Reddy (Columbia University)
DTSTART:20160727T103000Z
DTEND:20160727T110000Z
UID:TALK66858@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:A signature of many network datasets is strong local dependenc
 e\, a&nbsp\;phenomenon that&nbsp\;gives rise to frequently observed proper
 ties such as transitive triples\, pendants\, and structural heterogeneity.
  One difficulty in modeling such dependence is that the notion of locality
  may not be well-defined\, and it is likely to be heterogeneous throughout
  the network. Furthermore\, models that do not assume some form of conditi
 onal independence on the edges typically are intractable or too simplistic
  to serve as useful statistical models. We introduce a class of models\, b
 ased on random walks\, that allows the scale of dependence to vary\; it is
  able to generate a range of network structures faithful to those observed
  in real data\, and it admits tractable inference procedures.<br><br>This 
 is joint work with Peter Orbanz.
LOCATION:Seminar Room 1\, Newton Institute
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