Populations of Unlabelled Networks: Graph Space Geometry and Generalized Geodesic Regression Model
Event details
| Date | 31.03.2023 |
| Hour | 15:15 › 17:00 |
| Speaker | Anna Calissano, INRIA |
| Location | |
| 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
Organizer
- Tomas Masak
Contact
- Maroussia Schaffner