English

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

Social and Information Networks 2021-06-01 v2 Data Analysis, Statistics and Probability Machine Learning

Abstract

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 2\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.

Keywords

Cite

@article{arxiv.2105.04062,
  title  = {Approximate Fr\'echet Mean for Data Sets of Sparse Graphs},
  author = {Daniel Ferguson and François G. Meyer},
  journal= {arXiv preprint arXiv:2105.04062},
  year   = {2021}
}

Comments

28 pages

R2 v1 2026-06-24T01:55:34.741Z