Abstract
This paper compares modern approaches to draw complex graph data that create create compelling visualizations. Graphs are used to represent more and more complex systems that are used across various scientific domains. Some graph visualizations are used to model network topologies or service architectures in information service fields, model genomes in biomedicine or complex molecules in chemistry. Visualizations of graph data play important role in interpreting the meaning of graphed data. The adage “A picture is worth a thousand words” generally refers to the notion that a complex idea can be conveyed with just a single still image and that is actually the main reason for creating visualisations of any kind. Graph drawings from all domains follow the same rules covered by the graph theory. This work outlines different layout strategies used for drawing graph data. Tested graph drawing layouts are compared by standard quality measures to determine their suitability for various areas of usage.
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References
Di Battista, G., Eades, P., Tamassia, R., Tollis, I.: Algorithms for Drawing Graphs: an Annotated Bibliography. ACM (1994). doi:10.1016/0925-7721(94)00014-x
Graph, H.: Graph visualization and navigation in information visualization: A survey. Visualization and Computer Graphics. IEEE doi:10.1109/2945.841119
Purchase, H., Carrington, D., Allder, J.-A.: Empirical Evaluation of Aesthetics-based Graph Layout. Empirical Software Engineering. Kluwer (2002)
McGrath, C., Blythe, J., Krackhardt, D.: The effect of spatial arrangement on judgements and errors in interpreting graphs. Social Networks 19. Elsevier (1997)
Purchase, H.C., Hoggan, E., Görg, C.: How important is the mental map? – an empirical investigation of a dynamic graph layout algorithm. In: Kaufmann, M., Wagner, D. (eds.) GD 2006. LNCS, vol. 4372, pp. 184–195. Springer, Heidelberg (2007)
Di Battista, G., Gargb, A., Liottab, G., Tamassiab, R. ,Tassinaric, E. Vargiuc, F.: An experimental comparison of four graph drawing algorithms. Elsevier (1997). doi:10.1016/S0925-7721(96)00005-3
Kobourov, S.: Handbook of Graph Drawing and Visualization: Force-directed drawing algorithms (2013). http://cs.brown.edu/rt/gdhandbook/chapters/force-directed.pdf
Krzywinski, M.: Hive Plots - Linear Layout for Network Visualization - Visually Interpreting Network Structure and Content Made Possible (2011). http://www.hiveplot.net/
Holten, D.: Hierarchical, Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data (2006)
Riehmann, P., Hanfler, M., Froehlich, B.: Interactive Sankey Diagrams (2005)
Holik, F., Horalek, J., Marik, O., Neradova, S., Zitta, S.: The methodology of measuring throughput of a wireless network. In: CINTI 2014 (2014)
Godsil, C., Royle, G.: Algebraic Graph Theory. Springer (2001). ISBN 0-387-95241-1
Wattenberg, M.: Visual Exploration of Multivariate Graphs. ACM (2006). ISBN 1-59593-372-7
Bostock, M.: D3 Data-Driven Documents Documentations (2015). https://github.com/mbostock/d3/wiki
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Komarek, A., Pavlik, J., Sobeslav, V. (2015). Network Visualization Survey. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_27
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DOI: https://doi.org/10.1007/978-3-319-24306-1_27
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