Evaluation of Graph Sampling: A Visualization Perspective
- Submitting institution
-
Swansea University / Prifysgol Abertawe
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 31205
- Type
- D - Journal article
- DOI
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10.1109/TVCG.2016.2598867
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016)
- Article number
- -
- First page
- 401
- Volume
- 23
- Issue
- 1
- ISSN
- 1077-2626
- Open access status
- Not compliant
- Month of publication
- January
- Year of publication
- 2017
- 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
-
-
- Research group(s)
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-
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In data science, data analytics methods are often applied blindly without consideration of the visualisation technique to be used beforehand. However, effective visualisations are the primary means by which users understand their data. The presented human-centred studies provide evidence that the graph sampling technique chosen influences how the sample is perceived. Thus, the choice of analytics technique and visualisation is not a separable problem and must be considered jointly so that the user does not misinterpret the data. The experiments test graph sampling methods from Leskovec and Faloutsos (KDD 2006) which have wide use within the data science community.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -