Evaluation of Graph Sampling: A Visualization Perspective

Wu, Yanhong; Cao, Nan; Archambault, Daniel; Shen, Qiaomu; Qu, Huamin; Cui, Weiwei

doi:10.1109/TVCG.2016.2598867

Journal article 1177 views 807 downloads

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: Daniel Archambault

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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...

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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
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first_indexed	2016-11-24T14:27:27Z
last_indexed	2021-01-29T03:48:38Z
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spelling	2021-01-28T13:13:45.3516675 v2 31205 2016-11-24 Evaluation of Graph Sampling: A Visualization Perspective 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2016-11-24 SCS 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 Journal Article IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016) 23 1 401 410 Institute of Electrical and Electronics Engineers (IEEE) 1077-2626 Visualization, Measurement, Data visualization, Data mining, Fires, Scalability, Clustering algorithms 31 1 2017 2017-01-31 10.1109/TVCG.2016.2598867 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-01-28T13:13:45.3516675 2016-11-24T08:46:55.8516680 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yanhong Wu 1 Nan Cao 2 Daniel Archambault 0000-0003-4978-8479 3 Qiaomu Shen 4 Huamin Qu 5 Weiwei Cui 6 0031205-25112016075256.pdf graphsamplesub.pdf 2016-11-25T07:52:56.8870000 Output 10027944 application/pdf Accepted Manuscript true 2016-11-25T00:00:00.0000000 © 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 true eng
title	Evaluation of Graph Sampling: A Visualization Perspective
spellingShingle	Evaluation of Graph Sampling: A Visualization Perspective Daniel Archambault
title_short	Evaluation of Graph Sampling: A Visualization Perspective
title_full	Evaluation of Graph Sampling: A Visualization Perspective
title_fullStr	Evaluation of Graph Sampling: A Visualization Perspective
title_full_unstemmed	Evaluation of Graph Sampling: A Visualization Perspective
title_sort	Evaluation of Graph Sampling: A Visualization Perspective
author_id_str_mv	8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv	8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author	Daniel Archambault
author2	Yanhong Wu Nan Cao Daniel Archambault Qiaomu Shen Huamin Qu Weiwei Cui
format	Journal article
container_title	IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016)
container_volume	23
container_issue	1
container_start_page	401
publishDate	2017
institution	Swansea University
issn	1077-2626
doi_str_mv	10.1109/TVCG.2016.2598867
publisher	Institute of Electrical and Electronics Engineers (IEEE)
college_str	Faculty of Science and Engineering
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hierarchy_top_title	Faculty of Science and Engineering
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hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description	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
published_date	2017-01-31T03:38:06Z
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score	11.036116

Evaluation of Graph Sampling: A Visualization Perspective

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