Identification of influential invaders in evolutionary populations
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2361
- Type
- D - Journal article
- DOI
-
10.1038/s41598-019-43853-9
- Title of journal
- Scientific Reports
- Article number
- 7305
- First page
- -
- Volume
- 9
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2019
- URL
-
https://e-space.mmu.ac.uk/622908/
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
C - Machine Intelligence
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to suggest a novel computational way of identifying the most influential individuals in evolutionary populations. The methodology has been referenced in a series of other papers (eg XJ Zhang et al., Physica A, 540, 2020) and is used to discover the most influential co-operators in social networks in two further papers (including G. Yang et al., Sci. Rep. 11, 1127, 2021). The paper was written with Professor Matjaz Perc, one of the world’s leading researchers in complex systems and amongst the top 1% of cited physicists.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -