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SUMMARY:Populations of Unlabelled Networks: Graph Space Geometry and Gener
alized Geodesic Regression Model
DTSTART;VALUE=DATE-TIME:20230331T151500
DTEND;VALUE=DATE-TIME:20230331T170000
UID:a2050bf3c4cf734bb4b6f7a6ae4912537459e898aa7733d1c17be809
CATEGORIES:Conferences - Seminars
DESCRIPTION:Anna Calissano\, INRIA\nSets of graphs (or networks) arise in
many different fields\, from medicine to finance\, from sport to the socia
l sciences. The analysis of unlabelled graphs or networks is far from triv
ial due to the highly non-Euclidean nature of such data. We describe Graph
Space as a possible geometric embedding for a set of unlabelled graphs\,
i.e. graphs with no node correspondence across observations. Graph Space i
s a quotient space\, but it is not a manifold\, requiring the definition o
f statistical methods beyond the tangent space approach.\nWe introduce the
Align All and Compute algorithm and modify it for both estimating general
ised geodesic principal components and generalised geodesic regression mod
els\, showing how to interpolate between unlabelled graphs. We demonstrate
the flexibility of the framework on both simulated data\, public transpor
t system data and Fifa 2018 player passing network data.\n
LOCATION:GA 3 21 https://plan.epfl.ch/?room==GA%203%2021
STATUS:CONFIRMED
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