Everything is a Graph and Drawing It as Such is Always the Best Thing to Do

Well, maybe that’s going a little too far, but Sébastien Heymann has written an excellent response to the Ben Fry quote brought to the fore by Dan Brickley’s recent exploration of literary networks.

Ben Fry’s quote in full goes like this:

Graphs can be a powerful way to represent relationships between data, but they are also a very abstract concept, which means that they run the danger of meaning something only to the creator of the graph. Often, simply showing the structure of the data says very little about what it actually means, even though it’s a perfectly accurate means of representing the data. Everything looks like a graph, but almost nothing should ever be drawn as one.

There is a tendency when using graphs to become smitten with one’s own data. Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is “complex” fits just fine with the creator’s own interpretation of it. Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.

To which Heymann responds,

So graph visualization is not naturally worse compared to any data drawing: we just don’t teach how to read them in primary school. Do you remember the first time you saw a plot? I guess you find it really abstract. Most of the people don’t really know what to look at on a graph, and produce visualizations that don’t show something in particular. I personally think that it is a good thing, because put in context graph visualization is very young compared to other data drawings, and a language of networks that combine layout algorithms and visual variables is still in the making.

I think this sense of network literacy rings true. Graph visualization is incredibly appealing to audiences, but they’re unaccustomed to the fundamental concepts that underlay it, and so oftentimes the response to complex network visualization degrades from excitement to disdain, typified by a remark that acknowledges the aesthetic beauty but admits to little knowledge expression capability or frames, as Fry has, the visualization as a personal work–valuable to the creator but ultimately incapable of communicating the knowledge that creator claims to see in it.  This has been reinforced by traditional network analysis scholarship that cautions against visualizing large networks (in some cases, a large network is defined as 200 nodes, so we’re not even talking about Middling Data, here).

Seeing the evolution and adoption of spatial analysis by a broader and increasingly literate audience, I expect network analysis (and text analysis) to follow this track until, in three or five years (or months, product cycles being what they are) we’ll see a Google Graph with the ease-of-use and adoption that today’s Google Earth does.

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