Decentralized iterative approaches for community clustering in the networks
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1400
- Type
- D - Journal article
- DOI
-
10.1007/s11227-019-02765-1
- Title of journal
- The Journal of Supercomputing
- Article number
- -
- First page
- 4894
- Volume
- 75
- Issue
- 8
- ISSN
- 0920-8542
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
-
- 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
-
2
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The problem of identifying meaningful communities in large scale complex systems, is addressed by a novel algorithm that automatically clusters entities according to user-defined membership criteria, even in uncertain environments. The algorithm evaluation showed that it performed better than the current state of the art. This work is currently under review for funding as an approach to mitigate against health divides in response to the Covid-19 pandemic with Dr Stacey Todd (staceytodd@doctors.org.uk), a Consultant and Research Lead in Infectious Diseases at Royal Liverpool University Hospital and an NIHR NWC Research Scholar.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
