It's very easy to become enamoured with the visualisations and potential of network analysis, and see it as an end unto itself. This is one reason why I think that network analysis is a diagnostic methodology. It can aid understanding, but there are obvious limitations. For example any visualisation is a representation, or report of, data collected at particular time in a particular place. We all know human systems are dynamic, so it's reasonable to assume data will degrade and the network will change.

It is also a common mistake to think the visualisation, or the data matrix, represent analysis: they do not! I think Drew Conway , who is a political scientist, makes the point very well when he says:

"Simply because your data links people and you can visualize that, it does not mean you have performed network analysis. This is akin to displaying a line plot of some stock's price over a quarter and claiming you have performed statistical analysis-no-you have reported data. As with all other statistical processes, network analysis is meant to draw meaning and inference from the structure, which requires an understanding of these methodologies, their strengths and limitations".

To that I would add you must have a deep understanding of the network or organisation you are examining.

For those who want to examine some limitations of network analysis I recommend "Methodological Pitfalls in Social Network Analysis" by Nicholas Marschall. The central theme is that current methods produce questionable results. He deals specifically with the issues of missing data and network size reduction. I also recommend "Social Networks and Organizations" by Professors Martin Kilduff and Wenpin Tsai. This book is useful because it positions network analysis in the research traditions, and provides some discussion on the underlying theories. Understanding the foundations, strengths, weaknesses and limitations of networks analysis techniques matters!

Regards Graham