Decentralized iterative approaches for community clustering in the networks
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1260
- 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)
-
D - RCEEE
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work proposes novel Community Clustering techniques for Big Data that work with datasets distributed across several machines (existing techniques require the full dataset to be stored in a single machine) and with higher accuracy in identifying the clusters closer to the ground truth (existing techniques consider only the network topology, whereas the work includes both network topology and node attributes for clustering nodes). The work has resulted in an Innovate UK-KTP award ‘Guardian-AI’ (KTP011562, £242k to LJMU, 2019-2022) with production engineering company CAL International.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -