A general centrality framework based on node navigability
- Submitting institution
-
Birkbeck College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 185
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2019.2947035
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 2088
- Volume
- 32
- Issue
- -
- ISSN
- 1041-4347
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2019
- URL
-
http://eprints.bbk.ac.uk/id/eprint/30769/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
2 - Experimental Data Science
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This new framework for network centrality unifies several walk-based centrality measures, capturing the concept of node navigability, which is central to social search. Formal properties of the potential gain and scalable algorithms over large networks are presented, and are evaluated on social network instances taken from the Koblenz Network Collection (KONECT). A preliminary version of the paper introducing the potential gain as a novel composite centrality measure, appeared in WI '19: IEEE/WIC/ACM International Conference on Web Intelligence, 2019, which has an acceptance rate of 22.9%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -