Multivariate Network Exploration with JauntyNets

Ilir Jusufi, Andreas Kerren, Björn Zimmer

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

16 Citations (Scopus)

Abstract

The amount of data produced in the world every day implies a huge challenge in understanding and extracting knowledge from it. Much of this data is of relational nature, such as social networks, metabolic pathways, or links between software components. Traditionally, those networks are represented as node-link diagrams or matrix representations. They help us to understand the structure (topology) of the relational data. However in many real world data sets, additional (often multidimensional) attributes are attached to the network elements. One challenge is to show these attributes in context of the underlying network topology in order to support the user in further analyses. In this paper, we present a novel approach that extends traditional force-based graph layouts to create an attribute-driven layout. In addition, our prototype implementation supports interactive exploration by introducing clustering and multidimensional scaling into the analysis process.
Original languageEnglish
Title of host publicationProceedings - 2013 17th International Conference on Information Visualisation, IV 2013
PublisherIEEE
Pages19-27
Number of pages9
ISBN (Print)9780769550497
DOIs
Publication statusPublished - 2013
EventInternational Conference on Information Visualisation - London, United Kingdom
Duration: 16 Jul 201318 Jul 2013
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6656128

Publication series

NameProceedings of the International Conference on Information Visualisation
ISSN (Print)1093-9547

Conference

ConferenceInternational Conference on Information Visualisation
CountryUnited Kingdom
CityLondon
Period16.07.201318.07.2013
Internet address

Keywords

  • Attribute-driven layout
  • Force-based layouts
  • Graph drawing
  • Interaction
  • Multivariate networks
  • Network visualization
  • Visual analytics

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