The March/April issue of MIT Tech Review has an interesting article, “Between Friends: Sites Like Facebook Are Proving The Value of the Social Graph” (nods to Nathan Gilliat and Matthew Hurst -whose work is cited- for breaking this on their blogs).

Overall, this article provides some great examples of how sociograms - social network graphs - can be used to illustrate the structure of social media networks. It shows how network analysis can be used to predict how efficient a viral marketing effort will diffuse through two different types of networks. As shown in the following image, sparse, loosely structured types of networks may be poor candidates for viral strategies (recall the recent discussion about diffusion and small world networks?)

It also touches on social network analysis for measuring internal communications, and the different considerations of what constitutes a “link” among authors/ facebookers/ bloggers/ twitter-ers for measurement purposes. Overall, the article is a nice overview, and shows some of the interesting ways that social media communities mapped to understand their underlying structures. Compare them to examples from TouchGraph (a free website connection browser)…

… and Linkfluence (a commercial analytical provider based in France, that provides custom community map generation and tracking).

For the marketing or PR practitioners, these examples may seem somewhat academic; the question for them is usually “whom do I need to influence” and demonstrating effectiveness in influencing targets probably gets lost in large-scale maps. In contrast, Buzzlogic’s dashboard technology provides the ability to map and navigate the small-scale network graphs of individual influencer networks.

The TR article shows the value of online social network analysis as a strategic measurement tool. I think there’s a lot of room for growth and exploration; the TR topic doesn’t even touch on vizualizing the semantic association networks (content) of social media communication, or the power of animation to demonstrate change in a network over time (source: Stanford.edu / McFarland; you may have to reload this post to view the animation):

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