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|It is to be noted that the visualizations of social networks have been used to support SNA from the beginning Freeman L. C., (2000). Visualization of networks is more important because it is a natural way to communicate connectivity and allows for fast pattern recognition by human eyes. On the contrary, there are a number of challenges when visualizing networks (Battista et al., 1999; Herman et al., 2000). A number of layout algorithms discuss how to calculate the position of each and every node and the curve of each link in order to minimize link crossings and observe to visual principles. The algorithms are not large in number, and it is very difficult if the notes are very large then visualization is very difficult (Ham F. van, 2005).
Numerous techniques try to use available display more proficiently by distorting the graph/ network. One of the popular techniques is fisheye technique, which allows users to observe a focus area in detail, For example, read for further details (Munzner T., (1997). Another technique is Multiscale graph abstraction that reserves global structure, but navigation became difficult because clusters are obviously contracted and expanded, more information can be found in (Auber, et al, 2003; Parker G., G. Franck and C. Ware, 1998). Recent work associates these two techniques with fisheyeviews to decrease the number of displayed nodes while protecting the network structure (Gansner, E. R., Y. Koren and S. North, 2005). In addition, Ham Van and Van Wijk (2004) also combine distortion strategies for highly connected small-world networks.
A number of software tools designed to assist analysts to understand social networks, such as (Borgatti, S., M. G. Everett and L. C. Freeman, 2006; Brandes, U. and D. Wagner, 2003; de Nooy W., A. Mrvar and V. Batageli, 2005). The tools offer exciting techniques that users can use on networks. Though, the techniques are mostly a combination of statistical methods and visual output that put many analysts unclear about in what way and how to discover in a systematic manner.
|Visualizations of social networks have been used to aid SNA from the beginning [EN 13]. The visualization of networks is important because it is a natural way to communicate connectivity and allows for fast pattern recognition by humans. However, there are great challenges when visualizing networks [EN 9, EN 18]. There are many layout algorithms that attempt to calculate the position of each node and the curve of each link to minimize link crossings and adhere to aesthetic principles. These algorithms fall short, however, when the number of nodes is larger than several hundred and the large number of overlapping links makes it hard to judge connectivity [EN 31].
Several approaches attempt to more efficiently use available display space by distorting the graph. Fisheye techniques allow users to examine a focus area in great detail, but also tend to obscure the global structure of networks, e.g. [EN 21, EN 23]. Multiscale graph abstraction is another technique that preserves global structure, however navigation is difficult because clusters are explicitly contracted and expanded, e.g. [EN 2, EN 26]. Recent work combines these two approaches with topological fisheye views to reduce the number of displayed nodes while preserving the network structure [EN 14]. Van Ham and van Wijk also combine distortion strategies for highly connected, small-world networks [EN 32].
There are a number of software tools designed to help analysts understand social networks, such as [5, 7, 8]. These tools often feature an impressive number of analysis techniques that users can perform on networks. However, they are also often a medley of statistical methods and overwhelming visual output that leaves many analysts uncertain about how to explore in an orderly manner.
[EN 2] D. Auber, Y. Chiricota, F. Jourdan and G. Melancon, "Multiscale Visualization of Small World Networks", IEEE Symposium on Information Visualization, pp. 75-81, 2003.
[EN 5] S. Borgatti, M. G. Everett and L. C. Freeman, UCINET 6, Analytic Technologies, 2006.
[EN 7] U. Brandes and D. Wagner, "visone - Analysis and Visualization of Social Networks", Graph Drawing Software,in M. Junger and P. Mutzel, eds., Springer-Verlag, 2003.
[EN 8] W. de Nooy, A. Mrvar and V. Batageli, Exploratory Social Network Analysis with Pajek, Cambridge University Press, Cambridge, 2005.
[EN 9] G. Di Battista, P. Eades, R. Tamassia and I. G. Tollis, Graph Drawing: Algorithms for the Visualization of Graphs, Prentice Hall, New Jersey, 1999.
[EN 13] L. C. Freeman, "Visualizing Social Networks", Journal of Social Structure, 2000.
[EN 14] E. R. Gansner, Y. Koren and S. North, "Topological Fisheye Views for Visualizing Large Graphs", IEEE Transactions on Visualization and Computer Graphics, 11, pp. 457-468, 2005.
[EN 18] I. Herman, G. Melancon and M. S. Marshall, "Graph visualization and navigation in information visualization: A survey", IEEE Transactions on Visualization and Computer Graphics, 6, pp. 23-43, 2000.
[EN 19] H. Kang, C. Plaisant, B. Lee and B. B. Bederson, "NetLens: Iterative Exploration of Content-Actor Network Data", IEEE Symposium on Visual Analytics Science and Technology, 2006.
[EN 21] J. Lamping and R. Rao, "The hyperbolic browser: A Focus+Context Technique for Visualizing Large Hierarchies", Journal of Visual Languages and Computing, 6, 1995.
[EN 23] T. Munzner, "H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space", IEEE Symposium on Information Visualization, pp. 2-10, 1997.
[EN 26] G. Parker, G. Franck and C. Ware, "Visualization of Large Nested Graphs in 3D: Navigation and Interaction", Journal of Visual Languages and Computing, pp. 299-317, 1998.
[EN 31] F. van Ham, Interactive Visualization of Large Graphs, Technische Universiteit Eindhoven, 2005.
[EN 32] F. van Ham and J. J. van Wijk, "Interactive Visualization of Small World Graphs", IEEE Symposium on Information Visualization, 2004.
Slight modifications. Also all literature references stem from the source, which is not given anywhere in the thesis. et al is sometimes in italics, sometimes not.