Exploring density regions for analyzing dynamic graph data. (February 2018)

• Record Type: Journal Article
• Authors: Burch, Michael
• Is Part Of: Journal of visual languages & computing. Volume 44(2018)
• Page Start: 133
• Page End: 144
• Publication Date: 2018-02
• Subjects: Dynamic graph visualization -- Graph comparisons -- Edge splatting -- Isolines -- Big graphs; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.09.007
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Static or dynamic graphs are typically visualized by either node-link diagrams, adjacency matrices, adjacency lists, or hybrids thereof. In particular, for the case of a changing graph structure a viewer wishes to be able to visually compare the graphs in a sequence. Doing such a comparison task rapidly and reliably demands for visually analyzing the dynamic graph for certain dynamic patterns. In this paper we describe a novel dynamic graph visualization that is based on the concept of smooth density fields generated by first splatting the link information of a given graph in a certain layout or visual metaphor. To further visually enhance the time-varying graph structures we add user-adaptable isolines to the resulting dynamic graph representation. The computed visual encoding of the dynamic graph is aesthetically appealing due to its smooth curves and can additionally be used to do comparisons in a long graph sequence, i.e., from an information visualization perspective it serves as an overview representation supporting to start more detailed analysis processes. To demonstrate the usefulness of the technique we explore real-world dynamic graph data by taking into account visual parameters like visual metaphors, node-link layouts, smoothing iterations, number of isolines, and different color codings. In this extended work we additionally incorporate matrix and list splatting while also supporting the selection of density regions with overlaid link information. Moreover, from the selected graph the user can automatically apply region comparisons with other graphs based on global and local density properties. Such a feature is in particular useful for finding commonalities, hence serving as a special filtering function.