An Ensemble Approach to Link Prediction
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 58899163
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2017.2730207
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 2402
- Volume
- 29
- Issue
- 11
- ISSN
- 1041-4347
- Open access status
- Technical exception
- Month of publication
- July
- Year of publication
- 2017
- 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
-
4
- Research group(s)
-
B - Data Science and Artificial Intelligence
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a top data engineering journal. The ensemble enabled approach has significant advantages in terms of performance/scalability on various applications. Paper enhanced accurate link prediction significantly. Data graph model helped clean trading data, developed as a key part of an ESRC funded projects. It has helped London Capital Group (LCG) Ltd develop an innovative, intelligent real-time risk and data management system to produce a client risk prediction accuracy of 86%. The model developed was validated by applying it across all 65,000 markets operated by LCG and has significantly improved LCG’s risk management and profitability (contact: Risk Manager [LCG former]).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -