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Evaluation of Graph Sampling: A Visualization Perspective / Yanhong Wu; Nan Cao; Daniel Archambault; 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: Archambault, Daniel

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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 distribut...

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Published in: IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016)
ISSN: 1077-2626
Published: 2017
Online Access: Check full text

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
College: College of Science
Issue: 1
Start Page: 401
End Page: 410