Name
Abstract | ||
|---|---|---|
We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. 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 Su0026P100 index as well as climate data from US cities. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Social and Information Networks | Interdependence,Point estimation,Data mining,Computer science,Raw data,Synthetic data,Bayesian hierarchical modeling,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1808.06079 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Till Hoffmann | 1 | 16 | 1.94 |
| Leto Peel | 2 | 117 | 7.06 |
| Renaud Lambiotte | 3 | 920 | 64.98 |
| Nick Jones | 4 | 113 | 10.19 |