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A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0001-6150-0787
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0001-6745-4398
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0002-2901-935X
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA-VAESS)ORCID iD: 0000-0002-0519-2537
2021 (English)In: Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP / [ed] Christophe Hurter, Helen Purchase, José Braz, and Kadi Bouatouch, SciTePress, 2021, Vol. 3, p. 219-223Conference paper, Published paper (Refereed)
Abstract [en]

The visualization of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. This position paper contrasts the current trend of finding new ways of jointly embedding several properties with the alternative strategy of instead using, and combining, already existing state-of-the-art single scope embedding technologies. From this comparison, we argue that the latter strategy provides a more generic and flexible approach with several advantages. Hence, we hope to convince the visual analytics community to invest more work in resolving some of the key issues that would make this methodology possible.

Place, publisher, year, edition, pages
SciTePress, 2021. Vol. 3, p. 219-223
Keywords [en]
Multivariate Network, Visualization, Visual Analytics, Embedding, Methodology
National Category
Computer Sciences Computer and Information Sciences
Research subject
Computer Science, Information and software visualization; Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-100121DOI: 10.5220/0010314602190223ISI: 000661282300021Scopus ID: 2-s2.0-85102971461ISBN: 9789897584886 (print)OAI: oai:DiVA.org:lnu-100121DiVA, id: diva2:1518761
Conference
International Conference on Information Visualization Theory and Applications (IVAPP), Virtual Conference, 8-10 February, 2021
Available from: 2021-01-17 Created: 2021-01-17 Last updated: 2023-08-21Bibliographically approved

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Witschard, DanielJusufi, IlirMartins, Rafael MessiasKerren, Andreas

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