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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Estimating network edge probabilities by neighborh
ood smoothing - Elizaveta Levina (University of Mi
chigan)
DTSTART;TZID=Europe/London:20160714T113000
DTEND;TZID=Europe/London:20160714T120000
UID:TALK66753AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/66753
DESCRIPTION:Co-authors: Yuan Zhang (Ohio State Universit
y)\, Ji Zhu (University of Michigan)

The problem of estimating probabilities of net
work edges from the observed adjacency matrix has
important applications to predicting missing link
s and network denoising. It has usually been addr
essed by estimating the graphon\, a function that
determines the matrix of edge probabilities\, but
is ill-defined without strong assumptions on the
network structure. Here we propose a novel compu
tationally efficient method based on neighborhood
smoothing to estimate the expectation of the adja
cency matrix directly\, without making the strong
structural assumptions graphon estimation require
s. The neighborhood smoothing method requires lit
tle tuning\, has a competitive mean-squared error
rate\, and outperforms many benchmark methods on
the task of link prediction in both simulated and
real networks.

Related Links
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:info@newton.ac.uk
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