[Ml-stat-talks] Fwd: [ORFE-Seminars] November 27th, Wilks Statistics Seminar; Sherrerd Hall 101 at 12:30pm
Barbara Engelhardt
bee at princeton.edu
Tue Nov 21 14:04:47 EST 2017
Talk of interest on Monday.
**** Wilks Statistics Seminar ****
*DATE: * *Monday, November 27, 2017*
* TIME: *
*12:30 pm LOCATION: Sherrerd Hall 101 SPEAKER: Peter Orbanz,
Columbia University TITLE:* *Subsampling large
graphs and symmetry in networks*
*Abstract: *Consider a very large graph---say, the link graph
of a large social network. Now invent a randomized algorithm that extracts
a smaller subgraph. If we use the subgraph as sample data and perform
statistical analysis on this sample, what can we learn about the underlying
network? Clearly, that should depend on the algorithm. We approach the
problem by considering what distributional symmetries are satisfied by the
algorithm. There is a specific algorithm for which the induced symmetry is
precisely exchangeability. In this case, the appropriate statistical models
are so-called graphon models, but things change drastically if seemingly
minor modifications are made to the subsampler. I will discuss two types of
results: (1) How symmetry properties explain what we can learn from a
single sample. (2) Convergence properties of symmetric random variables:
Laws of large numbers, central limit theorems and Berry-Esseen type bounds,
which hold whether or not the symmetry property is derived from subsampling.
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