Populations of Unlabelled Networks: Graph Space Geometry and Generalized Geodesic Regression Model


Event details

Date 31.03.2023
Hour 15:1517:00
Speaker Anna Calissano, INRIA
Category Conferences - Seminars
Event Language English

Sets of graphs (or networks) arise in many different fields, from medicine to finance, from sport to the social sciences. The analysis of unlabelled graphs or networks is far from trivial 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 is a quotient space, but it is not a manifold, requiring the definition of statistical methods beyond the tangent space approach.
We introduce the Align All and Compute algorithm and modify it for both estimating generalised geodesic principal components and generalised geodesic regression models, showing how to interpolate between unlabelled graphs. We demonstrate the flexibility of the framework on both simulated data, public transport system data and Fifa 2018 player passing network data.

Practical information

  • Informed public
  • Free


  • Tomas Masak


  • Maroussia Schaffner