Modeling Network Populations via Graph Distances

Lunagomez Coria, Simon and Olhede, Sofia Charlotta and Wolfe, Patrick (2020) Modeling Network Populations via Graph Distances. Journal of the American Statistical Association. ISSN 0162-1459

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Abstract

This article introduces a new class of models for multiple networks. The core idea is to parametrize a distribution on labelled graphs in terms of a Fréchet mean graph (which depends on a user-specified choice of metric or graph distance) and a parameter that controls the concentration of this distribution about its mean. Entropy is the natural parameter for such control, varying from a point mass concentrated on the Fréchet mean itself to a uniform distribution over all graphs on a given vertex set. We provide a hierarchical Bayesian approach for exploiting this construction, along with straightforward strategies for sampling from the resultant posterior distribution. We conclude by demonstrating the efficacy of our approach via simulation studies and two multiple-network data analysis examples: one drawn from systems biology and the other from neuroscience.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the American Statistical Association
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 6 May 2020, available online: https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1763803
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
143859
Deposited By:
Deposited On:
12 May 2020 09:25
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Sep 2020 05:32