Approximate Fr\'echet Mean for Data Sets of Sparse Graphs

Autor:	Ferguson, Daniel, Meyer, François G.
Rok vydání:	2021
Předmět:	Computer Science - Social and Information Networks Physics - Data Analysis Statistics and Probability Statistics - Machine Learning
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 Fr\'echet mean. In this work, we equip a set of graph with the pseudometric defined by the $\ell_2$ 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 on sets of graphs. We describe an algorithm to compute an approximation to the Fr\'echet mean of a set of undirected unweighted graphs with a fixed size. Comment: 28 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.04062 Zobrazit plný text záznamu View this record from Arxiv