Anna Calissano
20/01/2021
Bio: Anna is a post doc in the Gstats Epione team at INRIA. She took her PhD between the Mox team of the Politecnico de Milano and DTU University in Cophenhagen. Her research interests regards the development of statistical tools for the analysis of populations of graphs.
Title: Populations of Unlabelled Networks: Graph Space Geometry and Geodesic Principal Components
Abstract: Populations of graphs are a complex and strongly non-Euclidean data type describing different relational phenomena in different fields. In this talk, we describe the statistical tools for the analysis of populations of unlabelled graphs, embedding them in the Graph Space, a quotient space of permuted adjacency matrices. After the overview of the geometrical framework and the different statistical techniques available, we will detail the Graph-valued regression model, a model with Euclidean inputs and Network-valued outputs. To estimate the defined intrinsic statistics in the Graph Space, we introduce an algorithm, namely Align All and Compute. These statistical tools are applied to quantify and analyse urban movements, in order to understand how people move within a square, a city, a region.
