Abstract
The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property. For sparse and small graphs, the most efficient approach to such visualization is node-link diagrams, whereas for dense graphs with attached data, adjacency matrices might be the better choice. Because graphs can contain both properties, being globally sparse and locally dense, a combination of several visual metaphors as well as static and dynamic visualizations is beneficial. In this paper, a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described. As the novelty of this technique, insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes. As an additional feature set, an automatic identification of groups, clusters, and outliers is provided over time, and based on the visual outcome of the node-link and matrix visualizations, the repertoire of the supported layout and matrix reordering techniques is extended, and more interaction techniques are provided when considering the dynamics of the graph data. Finally, a small user experiment was conducted to investigate the usability of the proposed approach. The usefulness of the proposed tool is illustrated by applying it to a graph dataset, such as e co-authorships, co-citations, and a Comprehensible Perl Archive Network distribution.
Highlights
Graph data, dynamic graph data, occur in various fields of application such as call dependencies in software engineering [1], friendship relations in social networks [2,3,4], areas of interest connections in eye tracking data [5], or traffic situations in road networks [6].Exploring such data requires advanced visual metaphors, in the best case, interactively linking several of such metaphors to benefit from the positive effects of all of them [7]
The node-link diagrams follow esthetic graph drawing criteria [15] whereas the adjacency matrices support the finding of different grouping and clustering patterns depending on the user tasks and which reordering strategy is requested
The proposed novel linked visualization strategy provides ways to adapt a view based on insight from other views; for example, clusters found in an adjacency matrix can be used to guide the layout of the node-link diagram and vice versa
Summary
Dynamic graph data, occur in various fields of application such as call dependencies in software engineering [1], friendship relations in social networks [2,3,4], areas of interest connections in eye tracking data [5], or traffic situations in road networks [6].Exploring such data requires advanced visual metaphors, in the best case, interactively linking several of such metaphors to benefit from the positive effects of all of them [7]. The node-link diagrams follow esthetic graph drawing criteria [15] whereas the adjacency matrices support the finding of different grouping and clustering patterns depending on the user tasks and which reordering strategy is requested.
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