lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Visual Analysis of Relationships between Heterogeneous Networks and Texts: An Application on the IEEE VIS Publication Dataset
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0003-3654-0255
RISE SICS.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2017 (English)In: Informatics, ISSN 2227-9709, Vol. 4, no 2, article id 11Article in journal (Refereed) Published
Abstract [en]

The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real world networks are multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related data sets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents, and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the IEEE VIS publications dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a Bag-of-Words index, and a word similarity matrix, all derived from the initial corpus and metadata. 

Place, publisher, year, edition, pages
2017. Vol. 4, no 2, article id 11
Keywords [en]
heterogeneous networks, interaction, graph drawing, multivariate data sets, NLP, text analysis, visualization, visual analytics
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-63188DOI: 10.3390/informatics4020011ISI: 000423667100005OAI: oai:DiVA.org:lnu-63188DiVA, id: diva2:1093800
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2017-05-08 Created: 2017-05-08 Last updated: 2018-06-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textVideo (Overview of the System)Video (Use Case)Supplementary material

Authority records BETA

Zimmer, BjörnKerren, Andreas

Search in DiVA

By author/editor
Zimmer, BjörnKerren, Andreas
By organisation
Department of Computer Science
Computer SciencesLanguage Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 136 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf