lnu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
MVN-Reduce: Dimensionality Reduction for the Visual Analysis of Multivariate Networks
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap (DV). (ISOVIS)ORCID-id: 0000-0002-2901-935X
University of Groningen, The Netherlands ; École Nationale de l’Aviation Civile, France.
University of São Paulo, Brazil.
University of Groningen, The Netherlands.
Visa övriga samt affilieringar
2017 (Engelska)Ingår i: EuroVis 2017 - Short Papers / [ed] Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll, Eurographics - European Association for Computer Graphics, 2017, s. 13-17Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The analysis of Multivariate Networks (MVNs) can be approached from two different perspectives: a multidimensional one, consisting of the nodes and their multiple attributes, or a relational one, consisting of the network’s topology of edges. In order to be comprehensive, a visual representation of an MVN must be able to accomodate both. In this paper, we propose a novel approach for the visualization of MVNs that works by combining these two perspectives into a single unified model, which is used as input to a dimensionality reduction method. The resulting 2D embedding takes into consideration both attribute- and edge-based similarities, with a user-controlled trade-off. We demonstrate our approach by exploring two real-world data sets: a co-authorship network and an open-source software development project. The results point out that our method is able to bring forward features of MVNs that could not be easily perceived from the investigation of the individual perspectives only. 

Ort, förlag, år, upplaga, sidor
Eurographics - European Association for Computer Graphics, 2017. s. 13-17
Nyckelord [en]
Multivariate Networks, Dimensionality Reduction, Visualization, Information Visualization, Visual Analytics, Network Visualization, Graph Drawing
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap, Informations- och programvisualisering
Identifikatorer
URN: urn:nbn:se:lnu:diva-62133DOI: 10.2312/eurovisshort.20171126ISBN: 978-3-03868-043-7 (digital)OAI: oai:DiVA.org:lnu-62133DiVA, id: diva2:1087400
Konferens
19th EG/VGTC Conference on Visualization (EuroVis '17), 12-16 June 2017, Barcelona, Spain
Tillgänglig från: 2017-04-06 Skapad: 2017-04-06 Senast uppdaterad: 2018-01-13Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextVideoFull text

Personposter BETA

Martins, Rafael MessiasKerren, Andreas

Sök vidare i DiVA

Av författaren/redaktören
Martins, Rafael MessiasKerren, Andreas
Av organisationen
Institutionen för datavetenskap (DV)
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 244 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf