No Cover Image

Journal article 1037 views 672 downloads

Evaluation of Graph Sampling: A Visualization Perspective

Yanhong Wu, Nan Cao, Daniel Archambault Orcid Logo, 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 Orcid Logo

  • graphsamplesub.pdf

    PDF | Accepted Manuscript

    © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

    Download (9.67MB)

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...

Full description

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

URI: https://cronfa.swan.ac.uk/Record/cronfa31205
Tags: Add Tag
No Tags, Be the first to tag this record!
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
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