Community detection in networks without observing edges

Till Hoffmann, Leto Peel, Renaud Lambiotte*, Nick S. Jones*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

20 Citations (Web of Science)

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.

Original languageEnglish
Article number1478
Number of pages11
JournalScience advances
Volume6
Issue number4
DOIs
Publication statusPublished - Jan 2020
Externally publishedYes

Keywords

  • VARIATIONAL BAYESIAN-INFERENCE
  • MODEL
  • MIXTURES

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