Rising Star Evaluation in Heterogeneous Social Network
- Submitting institution
-
De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11039
- Type
- D - Journal article
- DOI
-
10.1109/access.2018.2812923
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 29436
- Volume
- 6
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2018
- 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
-
3
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In this work, we develop a novel method to find rising stars in academic social network by mining network parameters and inner factors to train a decision tree for impacts evaluation. Experiment shows better performance than state-of-the-arts methods. The method can be applied for decision support, resource allocation, and other practical problems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -