Lunagómez, S;
Olhede, SC;
Wolfe, PJ;
(2020)
Modeling Network Populations via Graph Distances.
Journal of the American Statistical Association
10.1080/01621459.2020.1763803.
(In press).
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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 Frech\'{e}t 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 Frech\'{e}t 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 a multiple-network data analysis example drawn from systems biology.
| Type: | Article |
|---|---|
| Title: | Modeling Network Populations via Graph Distances |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/01621459.2020.1763803 |
| Publisher version: | https://doi.org/10.1080/01621459.2020.1763803 |
| Language: | English |
| Additional information: | Copyright © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10075816 |
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