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DTSTART:20190331T030000
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DTSTAMP:20191113T102749Z
UID:5c32fcfe5e3b9100996447@ist.ac.at
DTSTART:20190214T090000
DTEND:20190214T100000
DESCRIPTION:Speaker: Debarghya Ghoshdastidar\nhosted by Christoph Lampert\n
Abstract: Two-sample hypothesis testing for graphs is the statistical prob
lem of testing whether two given populations of graphs are similar or sign
ificantly different. It is an important tool in many scientific discipline
s including bioinformatics\, neuroscience and social science. For instance
\, testing between brain networks of Alzheimer patients and healthy indivi
duals reveal the neurological effects of Alzheimer's disease. The graph te
sting problem is quite challenging as one often needs to draw inference fr
om one or few samples of large graphs. In this talk\, we provide insights
into the fundamental challenges of the problem from a statistical (minimax
) perspective. We show that some standard formulations of the testing prob
lem are unsolvable if we observe only few samples. On the positive side\,
we present two problem formulations that are solvable even when we observe
only one or two graphs from each population. We also present new statisti
cal tests based on asymptotics of large random graphs\, and demonstrate th
e use of these methods in testing real networks.
LOCATION:Mondi Seminar Room 2\, Central Building\, IST Austria
ORGANIZER:tguggenb@ist.ac.at
SUMMARY:Hypothesis testing for graphs: Fundamental limits and practical met
hods
URL:https://talks-calendar.app.ist.ac.at/events/1727
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