mlsanityData to 7 Oct 2026

On the Number of Edges of the Frechet Mean and Median Graphs

arXiv 2105.14397 ↗First posted 30 May 2021math.CO cs.SI physics.data-ancited by 1

Not on the map. Why?

Abstract (arXiv)

The availability of large datasets composed of graphs creates an unprecedented need to invent novel tools in statistical learning for graph-valued random variables. To characterize the average of a sample of graphs, one can compute the sample Frechet mean and median graphs. In this paper, we address the following foundational question: does a mean or median graph inherit the structural properties of the graphs in the sample? An important graph property is the edge density; we establish that edge density is an hereditary property, which can be transmitted from a graph sample to its sample Frechet mean or median graphs, irrespective of the method used to estimate the mean or the median. Because of the prominence of the Frechet mean in graph-valued machine learning, this novel theoretical result has some significant practical consequences.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 30 May 2021.

Builds on

7 of 30 references.

References · 7

  1. 2019Metrics for Graph Comparison: A Practitioner's Guidecited by 37
  2. 2014Hypothesis Testing For Network Data in Functional Neuroimagingcited by 20
  3. 2019Modeling Network Populations via Graph Distancescited by 13
  4. 2017Network classification with applications to brain connectomicscited by 12
  5. 2014Nonparametric Bayes Modeling of Populations of Networkscited by 11
  6. 2016On clustering network-valued datacited by 9
  7. 2015Geometry of Graph Edit Distance Spacescited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.