No Cover Image

Conference Paper/Proceeding/Abstract 620 views 232 downloads

Proximity, Communities, and Attributes in Social Network Visualisation

Helen C. Purchase, Nathan Stirling, Daniel Archambault Orcid Logo

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Volume: 1, Pages: 65 - 72

Swansea University Author: Daniel Archambault Orcid Logo

DOI (Published version): 10.1109/asonam49781.2020.9381332

Abstract

The identification of groups in social networks drawn as graphs is an important task for social scientists whowish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the...

Full description

Published in: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
ISBN: 9781728110561
Published: IEEE 2020
URI: https://cronfa.swan.ac.uk/Record/cronfa55525
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2020-10-27T09:51:14Z
last_indexed 2021-04-30T03:20:20Z
id cronfa55525
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-04-29T14:00:39.0664601</datestamp><bib-version>v2</bib-version><id>55525</id><entry>2020-10-27</entry><title>Proximity, Communities, and Attributes in Social Network Visualisation</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>2020-10-27</date><deptcode>SCS</deptcode><abstract>The identification of groups in social networks drawn as graphs is an important task for social scientists whowish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.</abstract><type>Conference Paper/Proceeding/Abstract</type><journal>2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)</journal><volume>1</volume><journalNumber/><paginationStart>65</paginationStart><paginationEnd>72</paginationEnd><publisher>IEEE</publisher><placeOfPublication/><isbnPrint/><isbnElectronic>9781728110561</isbnElectronic><issnPrint/><issnElectronic/><keywords/><publishedDay>7</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-12-07</publishedDate><doi>10.1109/asonam49781.2020.9381332</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-04-29T14:00:39.0664601</lastEdited><Created>2020-10-27T09:44:33.2411393</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Helen C.</firstname><surname>Purchase</surname><order>1</order></author><author><firstname>Nathan</firstname><surname>Stirling</surname><order>2</order></author><author><firstname>Daniel</firstname><surname>Archambault</surname><orcid>0000-0003-4978-8479</orcid><order>3</order></author></authors><documents><document><filename>55525__18505__05059fc109ed4973a0c6e764608111b6.pdf</filename><originalFilename>ASONAM2020.pdf</originalFilename><uploaded>2020-10-27T09:50:25.8795519</uploaded><type>Output</type><contentLength>576762</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2020-12-07T00:00:00.0000000</embargoDate><documentNotes>IEEE copyright. Conference is Dec 7-10</documentNotes><copyrightCorrect>false</copyrightCorrect></document></documents><OutputDurs/></rfc1807>
spelling 2021-04-29T14:00:39.0664601 v2 55525 2020-10-27 Proximity, Communities, and Attributes in Social Network Visualisation 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2020-10-27 SCS The identification of groups in social networks drawn as graphs is an important task for social scientists whowish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout. Conference Paper/Proceeding/Abstract 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 1 65 72 IEEE 9781728110561 7 12 2020 2020-12-07 10.1109/asonam49781.2020.9381332 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-04-29T14:00:39.0664601 2020-10-27T09:44:33.2411393 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Helen C. Purchase 1 Nathan Stirling 2 Daniel Archambault 0000-0003-4978-8479 3 55525__18505__05059fc109ed4973a0c6e764608111b6.pdf ASONAM2020.pdf 2020-10-27T09:50:25.8795519 Output 576762 application/pdf Accepted Manuscript true 2020-12-07T00:00:00.0000000 IEEE copyright. Conference is Dec 7-10 false
title Proximity, Communities, and Attributes in Social Network Visualisation
spellingShingle Proximity, Communities, and Attributes in Social Network Visualisation
Daniel Archambault
title_short Proximity, Communities, and Attributes in Social Network Visualisation
title_full Proximity, Communities, and Attributes in Social Network Visualisation
title_fullStr Proximity, Communities, and Attributes in Social Network Visualisation
title_full_unstemmed Proximity, Communities, and Attributes in Social Network Visualisation
title_sort Proximity, Communities, and Attributes in Social Network Visualisation
author_id_str_mv 8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv 8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author Daniel Archambault
author2 Helen C. Purchase
Nathan Stirling
Daniel Archambault
format Conference Paper/Proceeding/Abstract
container_title 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
container_volume 1
container_start_page 65
publishDate 2020
institution Swansea University
isbn 9781728110561
doi_str_mv 10.1109/asonam49781.2020.9381332
publisher IEEE
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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
document_store_str 1
active_str 0
description The identification of groups in social networks drawn as graphs is an important task for social scientists whowish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.
published_date 2020-12-07T04:09:48Z
_version_ 1763753683273646080
score 11.035874

Similar Items