Community detection in networks without observing edges
File(s)
Published version
Author(s)
Hoffmann, Till
Peel, Leto
Lambiotte, Renaud
Jones, Nicholas
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.
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.
Date Issued
2020-01-22
Date Acceptance
2019-11-20
Citation
Science Advances, 2020, 6 (4)
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.
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
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://advances.sciencemag.org/content/6/4/eaav1478
Grant Number
EP/N014529/1
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
VARIATIONAL BAYESIAN-INFERENCE
MODEL
MIXTURES
cs.SI
cs.SI
cs.LG
physics.soc-ph
Publication Status
Published
Article Number
eaav1478
Date Publish Online
2020-01-24