Paper Info
Title
Visual Adjacency Lists for Dynamic Graphs
Abstract
We present a visual representation for dynamic, weighted graphs based on the concept of adjacency lists. Two orthogonal axes are used: one for all nodes of the displayed graph, the other for the corresponding links. Colors and labels are employed to identify the nodes. The usage of color allows us to scale the visualization to single pixel level for large graphs. In contrast to other techniques, we employ an asymmetric mapping that results in an aligned and compact representation of links. Our approach is independent of the specific properties of the graph to be visualized, but certain graphs and tasks benefit from the asymmetry. As we show in our results, the strength of our technique is the visualization of dynamic graphs. In particular, sparse graphs benefit from the compact representation. Furthermore, our approach uses visual encoding by size to represent weights and therefore allows easy quantification and comparison. We evaluate our approach in a quantitative user study that confirms the suitability for dynamic and weighted graphs. Finally, we demonstrate our approach for two examples of dynamic graphs.
Year
DOI
Venue
2014
10.1109/TVCG.2014.2322594
Visualization and Computer Graphics, IEEE Transactions  
Keywords
Field
DocType
data visualisation,graph theory,asymmetric mapping,compact representation,displayed graph,dynamic graphs,orthogonal axes,sparse graphs,visual adjacency lists,visual encoding,visual representation,visualization,weighted graphs,Graph visualization,adjacency lists,dynamic graphs,weighted graphs
Graph theory,Adjacency list,Graph drawing,Modular decomposition,Indifference graph,Computer science,Theoretical computer science,Graph product,1-planar graph,Maximal independent set
Journal
Volume
Issue
ISSN
20
11
1077-2626
Citations 
PageRank 
References 
19
0.65
13
Authors
3
Name
Order
Citations
PageRank
Marcel Hlawatsch11289.80
Michael Burch285466.47
Daniel Weiskopf32988204.30