As we have seen in class, graphs of networks can get messy real fast.  There can be hundreds of very small nodes, thousands of edges, different shaped nodes, colored edges, or directional edges all on top of a map of the United States.  It can often take several minutes of staring at a graph to recognize what is going on.  However, these graphs are essential in order to fully understand how a network functions. To avoid this clutter, comprehensive graphs must become even more organized and interactive.  

In a 2004 paper by Viégas and Donath of MIT, the topic of creating better graphs to comprehend ones social network is studied.  The paper can be found here:

<http://alumni.media.mit.edu/~fviegas/papers/viegas-cscw04.pdf>   
 

In their study, Viégas and Donath use two different types of interactive visualizations; Social Network Fragments (SNF) and PostHistory.  The SNF “is a traditional graph visualization that highlights clusters of contacts derived from the TO: and CC: lists in email archives.”  This is the type of social network that we have seen often in class, with people as nodes and friends joined with edges.  The authors describe the PostHistory graph as a metaphor of a calendar which shows how email relationships have evolved over time.  Both graphs contain over a hundred nodes, are very complex, and are shaded in several different colors.  However, the graphs are interactive and the user is able to zoom in on each graph and can actively change the viewing mode.

They found that the user still took some time to get used to the graph, but it also gave the user more insight to their social network than a graph printed on paper.  Additionally, they suggest that these graphs are still too cluttered with a large amount of email addresses shooting from each node.  Viégas and Donath state that “a general neglect of principles of good layout and visual perception on the part of the designers of these systems” is at fault for unorganized graphs.
 

Viégas and Donath also cite the work of Mark Lombardi, who created social network graphs of political scandals and criminal conspiracies.  Several of his works can be viewed here:

< http://www.pierogi2000.com/flatfile/lombardi.html>

Lombardi’s art is quite different from many of the social network graphs we see in class.  Much thought is placed on the position of a node, the curvature of an edge, and the succinct stories that define each relationship.  However, this extreme level of organization does come at a price.  The website above lists one graph as being over 10 feet wide.  This is simply not practical to distribute among a large number of people.
 

Clearly, one of the biggest challenges of creating a good network graph is finding a compromise between conveying the properties of a large network, and showing the detail of the individuals.  Hopefully, future advances in computer programming can help to avoid this dilemma by creating graphs that will not have to compromise between the two.