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Community detection in networks without observing edges

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Title: Community detection in networks without observing edges
Authors: Hoffmann, T
Peel, L
Lambiotte, R
Jones, N
Item Type: Journal Article
Abstract: We develop a Bayesian hierarchical model to identify communities of time series. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities.
Issue Date: 22-Jan-2020
Date of Acceptance: 20-Nov-2019
URI: http://hdl.handle.net/10044/1/74877
DOI: 10.1126/sciadv.aav1478
ISSN: 2375-2548
Publisher: American Association for the Advancement of Science
Journal / Book Title: Science Advances
Volume: 6
Issue: 4
Copyright Statement: © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N014529/1
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
Publication Status: Published
Article Number: eaav1478
Online Publication Date: 2020-01-24
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences