Abstract
Visualizing dynamic graphs is challenging due to the many data dimensions to be displayed such as graph vertices and edges with their attached weights or attributes and the additional time dimension. Moreover, edge directions with multiplicities and the graph topology are also important inherent features. However, in many dynamic graph visualization techniques each graph in a sequence is treated the same way, i.e., it is visually encoded in the same visual metaphor or even in the same layout. This visualization strategy can be problematic if the graphs are changing topologically over time, i.e., if a sparse graph becomes denser and denser over time or a star pattern is changing into a dense cluster of connected vertices. Such a dynamic graph data scenario demands for a visualization approach which is able to adapt the applied visual metaphor to each graph separately. In this paper we describe the dynamic graph wall to solve this problem by using multiple visual metaphors for dynamic graphs which are computed automatically by algorithms analysing each individual graph based on a given repertoire of graph features. The biggest issue in this technique for the graph dynamics, however, is the preservation of the viewer’s mental map at metaphor changes, i.e., to guide him through the graph changes with the goal to explore the data for time-varying patterns. To reach this goal we support the analyst by an interactive highlighting feature but we also display graphs in comparative metaphor rows to visually investigate the commonalities and differences over time.
Graphical Abstract
Graphical Abstract text
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Beck F, Burch M, Diehl S (2009) Towards an aesthetic dimensions framework for dynamic graph visualisations. In: Proceedings of the 13th International Conference on Information Visualisation, pp 592–597
Beck F, Burch M, Diehl S (2013) Matching application requirements with dynamic graph visualization profiles. In: Proceedings of 17th International Conference on Information Visualisation, pp 11–18
Beck F, Burch M, Diehl S, Weiskopf D (2014) The state of the art in visualizing dynamic graphs. In EuroVis State-of-the-Art Reports, EuroVis STAR
Beck F, Burch M, Diehl S, Weiskopf D (2016) A taxonomy and survey of dynamic graph visualization. Computer Graphics Forum
Burch M (2015) Dynamic graph visualization with multiple visual metaphors. In: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction, VINCI, pp 27–34
Burch M, Vehlow C, Beck F, Diehl S, Weiskopf D (2011) Parallel edge splatting for scalable dynamic graph visualization. IEEE Transa Visual Comp Graph 17(12):2344–2353
Burch S, Vehlow C, Konevtsova N, Weiskopf D (2011) Evaluating partially drawn links for directed graph edges. In: Proc. of Graph Drawing, pp 226–237
Burch M, Weiskopf D (2014) A flip-book of edge-splatted small multiples for visualizing dynamic graphs. In: Proceedings of the 7th International Symposium on Visual Information Communication and Interaction, VINCI, pp 29–36
Coleman TF, More JJ (1983) Estimation of sparse Jacobian matrices and graph coloring problems. SIAM J Numerical Anal 20(1):187–209
Diehl S, C Görg (2002) Graphs, they are changing. In Proceedings of 10th International Symposium on Graph Drawing, pp 23–30
Euler L (1741) Solutio problematis ad geometriam situs pertinentis. Commentarii Academiae Scientiarum Petropolitanae, 8:128–140
Frishman Y, Tal A (2004) Dynamic drawing of clustered graphs. In: Proceedings of 10th IEEE Symposium on Information Visualization, pp 191–198
Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Software Pract Exp 21(11):1129–1164
Ghoniem M, Fekete J, Castagliola P (2005) On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Info Visual 4(2):114–135
Henry N, Fekete J, McGuffin MJ (2007) Nodetrix: hybrid representation for analyzing social networks. CoRR, abs/0705.0599
Hlawatsch M, Burch M, Weiskopf D (2014) Visual adjacency lists for dynamic graphs. IEEE Trans Visual Comp Graph 20(11):1590–1603
Holten D (2006) Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans Visual Comput Graph 12(5):741–748
Huang W, Eades P, Hong S, Lin C (2013) Improving multiple aesthetics produces better graph drawings. J Visual Lang Comp 24(4):262–272
Panagiotidis A, Burch M, Deussen O, Weiskopf D, Ertl T (2014) Graph exploration by multiple linked metric views. In: Proceedings of 18th International Conference on Information Visualisation, IV, pp 19–26
Purchase HC, Cohen D, James MI (1995) Validating graph drawing aesthetics. In: Proceedings of the International Symposium on Graph Drawing, pp 435–446
Purchase HC, Hoggan E, Görg C (2007) How important is the mental map? An empirical investigation of a dynamic graph layout algorithm. In: Proceedings of the International Symposium on Graph Drawing, pp 262–273
Rosenholtz R, Li Y, Mansfield J, Jin Z (2005) Feature congestion: a measure of display clutter. In: Proceedings of Conference on Human Factors in Computing Systems, pages 761–770
Sugiyama K, Tagawa S, Toda M (1981) Methods for visual understanding of hierarchical system structures. IEEE Trans Syst Man Cybernet 11(2):109–125
Tversky B, Morrison JB, Bétrancourt M (2002) Animation: can it facilitate? Int J Human Comp Stud 57(4):247–262
von Landesberger T, Kuijper A, Schreck T, Kohlhammer J, van Wijk JJ, Fekete J, Fellner DW (2011) Visual analysis of large graphs: State-of-the-art and future research challenges. Computer Graphics Forum 30(6):1719–1749
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Burch, M. The dynamic graph wall: visualizing evolving graphs with multiple visual metaphors. J Vis 20, 461–469 (2017). https://doi.org/10.1007/s12650-016-0360-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-016-0360-z