Journal article 1177 views 807 downloads
Evaluation of Graph Sampling: A Visualization Perspective
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Qiaomu Shen,
Huamin Qu,
Weiwei Cui
IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016), Volume: 23, Issue: 1, Pages: 401 - 410
Swansea University Author:
Daniel Archambault
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PDF | Accepted Manuscript
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DOI (Published version): 10.1109/TVCG.2016.2598867
Abstract
Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have beenproposed to sample graphs, and the performance of these algorithms has been quantified through metrics based on graph structuralproperties preserved by the sampling: degree distribut...
| Published in: | IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016) |
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| ISSN: | 1077-2626 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa31205 |
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| Abstract: |
Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have beenproposed to sample graphs, and the performance of these algorithms has been quantified through metrics based on graph structuralproperties preserved by the sampling: degree distribution, clustering coefficient, and others. However, a perspective that is missing isthe impact of these sampling strategies on the resultant visualizations. In this paper, we present the results of three user studies thatinvestigate how sampling strategies influence node-link visualizations of graphs. In particular, five sampling strategies widely used inthe graph mining literature are tested to determine how well they preserve visual features in node-link diagrams. Our results showthat depending on the sampling strategy used different visual features are preserved. These results provide a complimentary view tometric evaluations conducted in the graph mining literature and provide an impetus to conduct future visualization studies |
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| Keywords: |
Visualization, Measurement, Data visualization, Data mining, Fires, Scalability, Clustering algorithms |
| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
401 |
| End Page: |
410 |