Network visualization
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Description
Networks themselves are simply a large amalgamation of enormous amounts of data that are interrelated. Network visualization is a pictorial representation and technique to present large amounts of data sets in complex networks [1]. The most common technique for visualizing networks us using nodes and links [2]. Typically, there are three types of visualizations: geographic, topological and plot-based visualizations. Geographic visualizations represent data with respect to their physical location of the nodes in the network [1]. Topological visualizations represent data that have relationships between nodes that are independent of the physical location of the nodes [1]. Finally, plot-based visualizations are pictorial representations over a period of time – similar to a line graph or histogram [1].
Geographic visualization is primarily used to represent the structure of the network. It is used to evaluate the physical location of nodes and links with respect to the rest of the network. Topological visualizations lay out the data independent of the physical location of the entities. Primarily, topological visualizations are simply node and link diagrams versus being restricted by the physical location of the nodes. Plot-based network visualization is more traditional pictorial representation of data similar to histograms, scatterplots and line-graphs. Plot-based network visualization typically focus on a specific point in time.
Network visualization displays the relationships among entities but sometimes, node and link diagrams become overwhelmed with the amount of data presented. To alleviate this problem, there are three common solutions: parameter focusing and dynamic interaction, clever node positioning to show structure, and 3D embeddings [2]. With network visualizations, graphs can be directed or undirected, weighted or unweighted [3]. The purpose of network visualization is to visualize large amounts of data to discover relationships that would not otherwise be seen. There are a number of computer programs that allow you to visualize your data – [Cytoscape] and [Network Workbench].
In addition to general network visualization, dynamic network visualization focuses more on change in the network. Smaller networks focus on detail elements of a graph structure while larger networks capture overall topology of the network [4]. Recently, the most popular networks to visualize are the social networks – i.e. Facebook™ and Twitter™. Visualcomplexity.com provides multiple examples of various networks. Facebook Visualizer gives an example of a graphical way to explore the Facebook social network.
The following graphical representation is The Atlas of Science project that achieves representation at the Hiberoamerican Science Research Facility.
Visualizing a Network
There are multiple steps needed in order to properly visualize a network. The first of which is data. Networks are suppose to represent large data sets that you couldn't otherwise understand or see relationships with. With raw data, you create a list, matrix or sparse matrix. You need to represent your data based on Source Node, Type of Interaction, and Target Node. Nodes and links are only connected based on a relationship so that relationship has to be defined. There are many ways to visualize data such as the following such as a random network, a source node network and degree sorted networks. Visualizations can be seen here: http://barabasilab.neu.edu/courses/phys5116/.
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Random Network -
Source Node Network -
Degree Sorted Network
List of softwares that are capable of visualizing complex networks:
Cytoscape, http://www.cytoscape.org/
Cytoscape is an open source software platform for visualizing complex-networks and integrating these with any type of attribute data. A lot of plugins are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.
Network Workbench, http://nwb.cns.iu.edu/
Network Workbench: A Large-Scale Network Analysis, Modeling and Visualization Toolkit for Biomedical, Social Science and Physics Research. This project will design, evaluate, and operate a unique distributed, shared resources environment for large-scale network analysis, modeling, and visualization, named Network Workbench (NWB). The envisioned data-code-computing resources environment will provide a one-stop online portal for researchers, educators, and practitioners interested in the study of biomedical, social and behavioral science, physics, and other networks.
NodeXL, http://nodexl.codeplex.com/
NodeXL: the Network Overview, Discovery and Exploration add-in for Excel 2007/2010 is a free and open extension to the familiar spreadsheet that enables the collection, analysis, and visualization of networks in general and social media networks from Twitter, YouTbe, Flickr, WWW, Facebook, email, and more.
GraphViz, http://www.graphviz.org/
Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains.
JGraph, http://www.jgraph.com/
JGraph and mxGraph are leading edge graph visualization component product families. They enable software developers to rapidly add complex and professional visualization networks.
JUNG, http://jung.sourceforge.net/
UNG — the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network.
Prefuse, http://prefuse.org/
Prefuse is a set of software tools for creating rich interactive data visualizations. The original prefuse toolkit provides a visualization framework for data sets.
*Descriptions of the above programs were taken from their respective websites.
REFERENCES:
[1] Withall, M., I. Phillips, and D. Parish, Network visualization: a review. Communications, IET, 2007. 1(3): p. 365-372.
[2] Eick, S., Aspects of network visualization. Computer Graphics and Applications, IEEE, 1996. 16(2): p. 69-72.
[3] Stasko, J., Graph and Network Visualization.
[4] Moody, J., D. McFarland, and S. Bender-deMoll, Dynamic network visualization1. ajs, 2005. 110(4): p. 1206-41.
[5] Bender-deMoll, S. and D.A. McFarland, The art and science of dynamic network visualization. Journal of Social Structure, 2006. 7(2).
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