A task-based evaluation of combined set and network visualization
- Submitting institution
-
University of Brighton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 7827214
- Type
- D - Journal article
- DOI
-
10.1016/j.ins.2016.05.045
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 58
- Volume
- 367-368
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2016
- 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
-
5
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Establishing which are the most effective tools for automatically visualising network data grouped into overlapping sets is an important task for social network analysis, in the production of gene expression data, and in crime analysis. This paper is significant because it establishes that EulerView and SetNet can be more effective for automatically visualising grouped network data than alternative tools i.e. Bubble Sets, KelpFusion and LineSets, in terms of accuracy and speed.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
