Community detection in networks with unobserved edges

Author: 

Hoffmann, T
Peel, L
Lambiotte, R
Jones, N

Publication Date: 

24 January 2020

Journal: 

Science Advances

Last Updated: 

2020-10-21T17:30:52.78+01:00

Issue: 

4

Volume: 

6

DOI: 

10.1126/sciadv.aav1478

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.

Symplectic id: 

909535

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article