Despite the terribly bland title that I've given this entry, there is actually much here that will be of use in learning how to transform a bipartite affiliation graph (i.e., poet X journal) into two separate one-mode graphs that show weighted connections between poets or journals based on their original connections. To be able to transform the graph in such a way allows us to see how poets are related to one another based on their level of participation in particular journals.
To start off, I took the original data culled from the "Modernist Poetry" reference and put it into three columns: Poet, Journal, and Strength (i.e., number of contributions to that journal). Thus for each instance where a poet contributed to a specific journal, I list the poet's unique ID, the journal's unique ID, and the number of times a contribution was made (i.e., "P1 | 1 | 2" indicates that Poet 1 contributed to Journal 1 exactly two times). See "ModPoetsGakkoVal.xls" to see what this looks like.
Because the UCINET program makes it easy to transform two-mode networks into one-mode networks, the next step was to get the data into a format that could be read by the program while also insuring that the labels and weights were input correctly. To do this, I simply needed to insert the proper commands at the head of the file "dl n=106, format=edgelist2"; indicate that the labels were already embedded; and then copy and paste the three columns of date created in Excel. Having done this, I was able to load the data into UCINET and produce a graph like this, with weights of edges embedded as attributes:
The red circles represent the poets and the blue squares represent the journals. The weights are not visible on this image as it would make it difficult to see much of anything.
Having gotten this far, I then took the UCINET file I had created and ran it through a function that split the graph into one-mode networks (see the following for instructions on how to do this). Specifically, I created a one-mode network showing only the poets and their weighted connection to one another. The weights were computed according to the sum of all minimum values shared by any two poets. Thus if P1 contributed to a journal 2 times and P5 contributed to that same journal 5 times, the weight would be resolved as 2. This value would then be computed for each instance in which the poets contributed to the same journal, and the sum of these minimums then became the weight linking the two individuals. The graph that resulted looked like this:
In other words, very messy. There's not much to be gleaned from this image alone, since it simply tells us that each poet is connected to every other poet. And this is to be expected given that our dataset was formed out of a list of individuals who all contributed at least once to the same journal. Where the graph becomes more interesting, or so I hope, is when we display the weights of the edges and start running analyses based on that information. I will begin to do this in the days ahead as I try to assess the kinds of algorithms that can be run on weighted, directed graphs, but for now I will leave you with this small tidbit.
Saving the above graph as a Pajek (.net) file, I loaded it into the Sci2 software so that I could use GUESS to play around with the colors and size of the edges (I find GUESS and CYTOSCAPE provide a much more intuitive interface for doing this than does NETDRAW). I then trimmed (or hid) all the edges with a weight less than 70, which I should note was a purely arbitrary choice. I'll play around with this upper limit in the future. What resulted was this:
As you can see, most of the nodes are now isolated and all that remains is a core group of five nodes with two outliers connected to the core. Changing the visualization slightly to reflect the weights of the edges, we get this image:
The next step, of course, is to see who these remaining individuals are. I'll leave this for a later entry. I think it would also be useful to think some more about how the calculation of weights might or might not translate to the reality we are ultimately trying to capture. In real historical terms, what does it mean to say that the value of the connection between two poets contributing to the same journal is only as strong as the "weaker" of the two?
