A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks

Daniel Witschard, Ilir Jusufi, Rafael M. Martins, Andreas Kerren

2021

Abstract

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.

Paper Citation

in Harvard Style

Witschard D., Jusufi I., Martins R. and Kerren A. (2021). A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP; ISBN 978-989-758-488-6, SciTePress, pages 219-223. DOI: 10.5220/0010314602190223

in Bibtex Style

@conference{ivapp21,
author={Daniel Witschard and Ilir Jusufi and Rafael M. Martins and Andreas Kerren},
title={A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP},
year={2021},
pages={219-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010314602190223},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP
TI - A Statement Report on the Use of Multiple Embeddings for Visual Analytics of Multivariate Networks
SN - 978-989-758-488-6
AU - Witschard D.
AU - Jusufi I.
AU - Martins R.
AU - Kerren A.
PY - 2021
SP - 219
EP - 223
DO - 10.5220/0010314602190223
PB - SciTePress