An Efficient Evolutionary User Interest Community Discovery Model in Dynamic Social Networks for Internet of People
- Submitting institution
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University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22754089
- Type
- D - Journal article
- DOI
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10.1109/JIOT.2019.2893625
- Title of journal
- IEEE Internet of Things Journal
- Article number
- -
- First page
- 9226
- Volume
- 6
- Issue
- 6
- ISSN
- 2327-4662
- Open access status
- Technical exception
- Month of publication
- January
- 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
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5
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Introduces a new model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery. This work was a catalyst for a collaboration with the School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, China, resulting in two PhD projects (Liang Jiang and Leilei Shi) and a follow-on publication in IEEE Transactions on Computational Social Systems (doi 10.1109/TCSS.2019.2938954).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -