Guiding graph exploration by combining layouts and reorderings
| Autor: | Kiet Bennema ten Brinke, Sebastiaan Peters, Adrien Castella, Ghassen Karray, Michael Burch, Rinse Vlaswinkel, Vasil Shteriyanov |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Theoretical computer science
Computer science Relational database Novelty 020207 software engineering 0102 computer and information sciences 02 engineering and technology 01 natural sciences Graph 010201 computation theory & mathematics Graph drawing Scalability 0202 electrical engineering electronic engineering information engineering Adjacency matrix |
| Zdroj: | VINCI |
| DOI: | 10.1145/3430036.3430064 |
| Popis: | Visualizing graphs is a challenging task due to the various properties of the underlying relational data. For sparse and small graphs the perceptually most efficient way are node-link diagrams whereas for dense graphs with attached data, adjacency matrices might be the better choice. Since graphs can contain both properties, being globally sparse and locally dense, a combination of several visualizations is beneficial. In this paper we describe a visually and algorithmically scalable approach to provide views and perspectives about graphs as interactively linked node-link as well as adjacency matrix visualizations. The novelty of the technique is that insights like clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the others. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by taking into account several layout and reordering properties in combination as well as different edge properties for the same set of nodes. We illustrate the usefulness of our tool by applying it to graph datasets like co-authorships, co-citations, and a CPAN distribution. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|