As the world becomes more connected, whether through email, social web sites, chat, or instant messaging, there is now a need to understand this interconnectivity. These networks could be computer networks, the connectedness of genes and chromosomes, proteins, or even citations of papers. The need is not new because networks are not new. There have been networks connecting people through phone calls and postal letters, for example. What is new is the many different methods of connection and the size of networks. Given these large networks, how might you go about understanding their properties? Further, how might you go about gaining insight into the types of connections, the strong versus weak connections, the permutations of connections and other such features of networks?
If you were to try to simply list the connections, say in a table that showed a person and who he sent email to, that table would quickly grow. You would have gained little insight to the network except maybe to see just how vast it is. The natural method, then, is to find a graph, a picture, of the network. In Visual Complexity, Manuel Lima starts to show us different pictures we can use to see networks.
He begins with a short history of networks and graphs some dating back centuries. If you gain nothing else from that you will at least see that the need for insight is quite old. Lima does a satisfactory job of describing this history and his illustrations are excellent supports for the text. He then goes on to show more examples and as he does so, the textual explanations slowly fall away. In fact, by page 97 (out of a total of 272 pages) there is very little text but lots of pictures.
If you had to pick a point where the book fails, it's page 97 at the start of chapter 04 (his numbering includes a leading zero) entitled "Infinite Interconnectedness." At this point Lima simply shows network diagrams of nations, authors and papers, email messages, proteins. Lima goes on a roll so that at Chapter 05 we have pictures of the "European Academic Network", campaign donation networks, and even a diagram of James Joyce's language usage. The topics are quite varied, but the pictures are impossible to read and there's no explanation as to how one can understand these hideous and over blown graphics. They are cramped, impossible to read, and impossible to understand. The pictures deserve explanations and details as to how you can understand them and see what it is the creators are trying to say. Unfortunately, Lima does not tell us what the graphics mean, how we should understand them, and what, if anything, there is to be gained by looking at them.
It's a shame that Lima let his readers down. I would think that there is much to learn from the selected graphs and much to take away for our own use. But, Lima does not say and with the pictures as they are, we are left on our own with no way to interpret the information.
With all due respect to Lima, before you buy this book, you should look to the books on graphics and data display by Edward Tufte. There you'll find fantastic graphs and pictures with detailed explanations so that you can learn how to make your own work better. Lima' book won't do that for you.