In this section
@article{Hoffmann:2020:10.1126/sciadv.aav1478,
author = {Hoffmann, T and Peel, L and Lambiotte, R and Jones, N},
doi = {10.1126/sciadv.aav1478},
journal = {Science Advances},
title = {Community detection in networks without observing edges},
url = {http://dx.doi.org/10.1126/sciadv.aav1478},
volume = {6},
year = {2020}
}
TY - JOUR
AB - We develop a Bayesian hierarchical model to identify communities of time series. Fitting the model provides an end-to-end community detection algorithmthat does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approachnaturally supports multiscale community detection as well as the selection ofan optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of theS&P100 index as well as climate data from US cities.
AU - Hoffmann,T
AU - Peel,L
AU - Lambiotte,R
AU - Jones,N
DO - 10.1126/sciadv.aav1478
PY - 2020///
SN - 2375-2548
TI - Community detection in networks without observing edges
T2 - Science Advances
UR - http://dx.doi.org/10.1126/sciadv.aav1478
UR - https://advances.sciencemag.org/content/6/4/eaav1478
UR - http://hdl.handle.net/10044/1/74877
VL - 6
ER -