Theoretical analysis and computation of the sample Frechet mean for sets of large graphs based on spectral information

Autor:	Ferguson, Daniel, Meyer, Francois G.
Rok vydání:	2022
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Computer Science - Social and Information Networks Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	To characterize the location (mean, median) of a set of graphs, one needs a notion of centrality that is adapted to metric spaces, since graph sets are not Euclidean spaces. A standard approach is to consider the Frechet mean. In this work, we equip a set of graphs with the pseudometric defined by the norm between the eigenvalues of their respective adjacency matrix. Unlike the edit distance, this pseudometric reveals structural changes at multiple scales, and is well adapted to studying various statistical problems for graph-valued data. We describe an algorithm to compute an approximation to the sample Frechet mean of a set of undirected unweighted graphs with a fixed size using this pseudometric. Comment: arXiv admin note: text overlap with arXiv:2105.04062
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.05923 Zobrazit plný text záznamu View this record from Arxiv