TaMax: Visualizing Dense Multivariate Networks with Adjacency Matrices

tamax poster screenshot

Abstract

Considering node and edge attribute is crucial for many network exploration and analysis tasks. However, effective visualization of both structure and attributes is a challenging problem, especially for dense graphs. In this poster, we introduce TaMax, a technique designed to visualize dense multivariate graphs with a diverse set of node and edge attributes based on adjacency matrices. In TaMax, node attributes are visualized in a table that is juxtaposed with the matrix, while edge attributes visualized in the cells. We investigate different ways to visualize multiple edge attributes: dividing each cells into sub-cells showing different edge attributes or overlaying a secondary attribute with opacity over a cell. Furthermore, TaMax addresses the scalability problem by allowing flexible grouping based on node attributes and querying based on edge attributes.

Citation

Ilkin Safarli, Alexander Lex
TaMax: Visualizing Dense Multivariate Networks with Adjacency Matrices
Proceedings of the IEEE Information Visualization Conference - Posters (InfoVis ’19), 2019.

BibTeX

@inproceedings{2019_infovis_tamax,
  title = {TaMax: Visualizing Dense Multivariate Networks with Adjacency Matrices},
  author = {Ilkin Safarli and Alexander Lex},
  booktitle = {Proceedings of the IEEE Information Visualization Conference - Posters (InfoVis ’19)},
  year = {2019}
}