Community detection in networks without observing edges
(2020) Science advances — Vol. 6, n° 4 (2020)
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- Authors
Hoffmann, Till 
Peel, Leto Lambiotte, Renaud Jones, Nick S.
- 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 and 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 and climate data from U.S. cities. Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
- Affiliations
Citations
Hoffmann, T., Peel, L., Lambiotte, R., & Jones, N. S. (2020). Community detection in networks without observing edges. Science advances, 6(4). https://doi.org/10.1126/sciadv.aav1478 (Original work published 2020)