Network Visualization by David Shelley

Network Visualizationby David Shelley Some slides adapted from Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Outline • Why visualize large networks? 2. Issues when Graphing Large Networks Production Issues Layout Issues 3. Common solutions to graphing large networks 4. Conclude with common tools

Outline • Why visualize large networks? 2. Issues when Graphing Large Networks Production Issues Layout Issues 3. Common solutions to graphing large networks

Why visualize large networks? “There is nothing better than a picture for making you think of questions you had forgotten to ask (even mentally)” Tukey and Tueky, 1985 “Finding ways to visualize datasets can be as important as ways to analyze them.” Ripley 2005 “Data visualization is good for data cleaning, for exploring data, for identifying treads and clusters, for spotting local patterns, for evaluating modelling output and for presenting resutls. Visualization is essential for Exploratory Data Analysis.” Unwin et. Al. Quotes found in Graphics of Large Datasets, Visualizing a Million by Unwin et. Al.

Why visualize large networks? • Discover anomalies in the data

Why visualize large networks? • Understand the flow of a network Metro Network of Washington DC Internet Service Providers

Why visualize large networks? Map of Springfield by Jerry Lerma and Terry Hogan Understand the relation between geographical objects How do I get to Moe’s ?

Why visualize large networks? • Use it to find socioeconomic patters. 1981 1992 http://www.mpi-fg-koeln.mpg.de/~lk/netvis/trade/WorldTrade.html

Why visualize large networks? • Conclusion: Discover anomalies. Understand the flow of a network. Understand the relation between geographical objects. Use it to find socioeconomic patters. Many other reasons not mentioned.

Issues when Graphing Large NetworksPRODUCTION ISSUES Storage Hard disk space. RAM (memory). File formats of data. Google’s First Production Server It is not publically known but Wikipedia estimates that Google maintains over 450,000 servers. Source: http://flickr.com/photos/jurvetson/157722937/ Graphics of large Datasets Visualizing a Million (Antony Unwin, Martin Theus Heike Hofmann)

Issues when Graphing Large NetworksPRODUCTION ISSUES Quality The larger the network, the higher possibility of errors in the data. Complexity (meaning many not Big-O) This is a major problem with large networks. More variables, more detail, more categories. Speed Currently we are interested in getting results from our graph fast enough to be considered interactive. Analysis What algorithms are used. What order of complexity is required for the algorithms. Graphics of large Datasets Visualizing a Million (Antony Unwin, Martin Theus Heike Hofmann)

Issues when Graphing Large NetworksPRODUCTION ISSUES Display The more nodes there are the more pixels on the screen you will need. The more information that needs to be presented on the screen the more window design and window management become increasingly important.

Issues when Graphing Large NetworksPRODUCTION ISSUES Display 800 x 600 = 480,000 pixels 1024 x 768 = 786,432 pixels 1920 x 1200 = 2,304,000 pixels Not enough pixels to display all the nodes!!!

Issues when Graphing Large NetworksPRODUCTION ISSUES • Conclusion of production issues: Physical Memory Issues. Quality of Data Issues. Complexity of each element in the graph (not talking about Big-O). Speed of loading and handling all the elements Analyzing the large data set. Finder better algorithms. Display overload. Not enough pixels on a single screen.

Issues when Graphing Large NetworksLAYOUT ISSUES How to represent an edge? Labels on Edges Thickness of Edges A Color of Edge Shape of Edges Directed Edges

Issues when Graphing Large NetworksLAYOUT ISSUES The problem with edges is they can occlude other parts of the graph!!! Before drawing edges After drawing edges

Issues when Graphing Large NetworksLAYOUT ISSUES How to represent a node? Shapes of Nodes Size of Nodes Color of Nodes A Labels of Nodes Location of Nodes B

Issues when Graphing Large Networks Conclusion: Production Issues Storage Quality Complexity Speed Analysis Layout Issues How to represent and edge How to represent a node

Common solutions to graphing large networks Draw important objects on top of other objects. Notice how the nodes have been covered up by edges.

Common solutions to graphing large networks Aesthetic Considerations Minimize lines crossing. Non-overlapping. Scale edge lengths. VS VS VS

Common solutions to graphing large networks Aesthetic Considerations

Common solutions to graphing large networks Use alpha-blending

Common solutions to graphing large networks Layout Algorithms Planar layout Tree layout Circular/Spiral And there’s more…

Common solutions to graphing large networks Layout Algorithms Dynamic Networks Kamada-Kawai (KK) (spring embedder) Fruchterman-Reingold (FR) Force And there’s more… Force Layout Methods such as the Spring Model http://java.sun.com/applets/jdk/1.4/demo/applets/GraphLayout/example1.html Java Universal Network/Graph Framework (JUNG) http://jung.sourceforge.net/applet/showlayouts.html

Common solutions to graphing large networks User Interaction Theus (1996) and Unwin (1999) have proposed there are three broad components of interaction for statistical graphics. 1. Querying 2. Selection and linking 3. Varying plot characteristics

Common solutions to graphing large networks User Interaction Theus (1996) and Unwin (1999) have proposed there are three broad components of interaction for statistical graphics. 1. Querying http://adn.blam.be/springfield/

Common solutions to graphing large networks User Interaction Theus (1996) and Unwin (1999) have proposed there are three broad components of interaction for statistical graphics. 1. Querying 2. Selection and linking Select View Stats View AT&T Sprint

Common solutions to graphing large networks User Interaction Theus (1996) and Unwin (1999) have proposed there are three broad components of interaction for statistical graphics. 1. Querying 2. Selection and linking

Common solutions to graphing large networks User Interaction Theus (1996) and Unwin (1999) have proposed there are three broad components of interaction for statistical graphics. 1. Querying 2. Selection and linking 3. Varying plot characteristics Sort View Spanish View AT e T AT&T Sprint Esprint

Common solutions to graphing large networks User Interaction Focus + Context • Basic Idea: • Show selected regions of interest in greater detail (focus) • Preserve global view at reduced detail (context) • NO occlusion • All information is visible simultaneously Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Common solutions to graphing large networks User Interaction Focus + Context Alternative Names for Focus + Context • Fisheye views • Fisheye lens • Continuously variable zoom • Nonlinear magnification • Hyperbolic views • Distortion viewing • Rubber sheet views • … Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Common solutions to graphing large networks User Interaction Focus + Context Applications for Focus + Context • Visualization of Networks/Graphs • Viewing text • Image/Document viewing • Cartography • Cluster Visualization Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Common solutions to graphing large networks User Interaction Focus + Context Types of Focus + Context • Spatial • One Dimensional • Easy to apply and understand • Two Dimensional • Most common, operating on 2D layouts of information • Three Dimensional • Less common • Logical • Effect applies to logical structure of the information • Combined Spatial and Logical • Data Driven Magnification Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Example of MoireGraph http://www.cse.msstate.edu/~tjk/publications/papers/tjk-infovis03.pdf

Common solutions to graphing large networks User Interaction Focus + Context Limitations • Limited degree of magnification? • 10X Maximum? • Open research question • Disorientation • Complex transformations might cause viewer to get lost • Need effective visual cues to avoid this Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Common solutions to graphing large networks User Interaction Focus + Context Strengths • Mirrors the way the visual cortex is designed • Good navigation tool for interactively exploring data • probe regions of interest before committing to navigating to them (easily reversible) • Can be combined with other viewing paradigms such as Pan and Zoom Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Common solutions to graphing large networks User Interaction Focus + Context Alternatives • Pan&Zoom • Scales to high factors • Navigation can be a problem • Multiple views at different scales • No distortion between scales • No continuity either Visualization 2003 - Network Visualization Course - T. Alan Keahey -www.visintuit.com

Network Visualization by David Shelley

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