No Cover Image

Journal article 583 views 227 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

  • 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!
first_indexed 2016-11-24T14:27:27Z
last_indexed 2021-01-29T03:48:38Z
id cronfa31205
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-01-28T13:13:45.3516675</datestamp><bib-version>v2</bib-version><id>31205</id><entry>2016-11-24</entry><title>Evaluation of Graph Sampling: A Visualization Perspective</title><swanseaauthors><author><sid>8fa6987716a22304ef04d3c3d50ef266</sid><ORCID>0000-0003-4978-8479</ORCID><firstname>Daniel</firstname><surname>Archambault</surname><name>Daniel Archambault</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2016-11-24</date><deptcode>SCS</deptcode><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</abstract><type>Journal Article</type><journal>IEEE Transactions on Visualization and Computer Graphics (InfoVis 2016)</journal><volume>23</volume><journalNumber>1</journalNumber><paginationStart>401</paginationStart><paginationEnd>410</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1077-2626</issnPrint><issnElectronic/><keywords>Visualization, Measurement, Data visualization, Data mining, Fires, Scalability, Clustering algorithms</keywords><publishedDay>31</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2017</publishedYear><publishedDate>2017-01-31</publishedDate><doi>10.1109/TVCG.2016.2598867</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-01-28T13:13:45.3516675</lastEdited><Created>2016-11-24T08:46:55.8516680</Created><path><level id="1">College of Science</level><level id="2">Computer Science</level></path><authors><author><firstname>Yanhong</firstname><surname>Wu</surname><order>1</order></author><author><firstname>Nan</firstname><surname>Cao</surname><order>2</order></author><author><firstname>Daniel</firstname><surname>Archambault</surname><orcid>0000-0003-4978-8479</orcid><order>3</order></author><author><firstname>Qiaomu</firstname><surname>Shen</surname><order>4</order></author><author><firstname>Huamin</firstname><surname>Qu</surname><order>5</order></author><author><firstname>Weiwei</firstname><surname>Cui</surname><order>6</order></author></authors><documents><document><filename>0031205-25112016075256.pdf</filename><originalFilename>graphsamplesub.pdf</originalFilename><uploaded>2016-11-25T07:52:56.8870000</uploaded><type>Output</type><contentLength>10027944</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><action/><embargoDate>2016-11-25T00:00:00.0000000</embargoDate><documentNotes>&#xA9; 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</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
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 College of 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 College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
document_store_str 1
active_str 0
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 2017-01-31T03:53:49Z
_version_ 1703143307986599936
score 10.802645