Generative Graph Prototypes from Information Theory
- Submitting institution
-
University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54874343
- Type
- D - Journal article
- DOI
-
10.1109/TPAMI.2015.2400451
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 2013
- Volume
- 37
- Issue
- 10
- ISSN
- 0162-8828
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- 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)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in one of the top CS journals, this is the first generative model of graph structure which is able to control model complexity using an information criterion. This is a major output of EU FET project SIMBAD, rated excellent by all three external reviews.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -