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  • Modeling Network Populations via Graph Distances

    Rights statement: 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

    Accepted author manuscript, 1.69 MB, PDF document

    Embargo ends: 6/05/21

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Modeling Network Populations via Graph Distances

Research output: Contribution to journalJournal article

E-pub ahead of print
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<mark>Journal publication date</mark>6/05/2020
<mark>Journal</mark>Journal of the American Statistical Association
Publication StatusE-pub ahead of print
Early online date6/05/20
<mark>Original language</mark>English

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.

Bibliographic note

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