A Pólya urn approach to information filtering in complex networks
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14608
- Type
- D - Journal article
- DOI
-
10.1038/s41467-019-08667-3
- Title of journal
- Nature Communications
- Article number
- ARTN 745
- First page
- -
- Volume
- 10
- Issue
- 1
- ISSN
- 2041-1723
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
https://static-content.springer.com/esm/art%3A10.1038%2Fsrep08190/MediaObjects/41598_2015_BFsrep08190_MOESM1_ESM.doc
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces a novel technique to extract network “backbones”, i.e., sets of statistically relevant links from large network datasets. Its main significance lies in its change of paradigm, as other available backbone extraction techniques are based on statistical tests that compared a network’s properties with those of a random counterpart. Conversely, the technique published in this paper extracts backbones whose significance is determined against the specific properties of a given network under study. Applications of the technique showcased in the paper include a methodology to predict global trade flows.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -