It has been a while since I presented my first visualizations of Ibn Tuluns manuscript corpus with Palladio. Palladio is an easy-to-learn tool that works in your browser and thus is independent of your operating system. You just put your data in several simple tables and upload them there. Then you create the relevant connections between the tables in the interface. Basically, all you need is one table for the entities to be connected (nodes) and one for the connections between them (edges).
Also, you can add a second table with a different sets of nodes. In my case, I wanted to know which of Ibn Tulun’s works could be found in which manuscripts. As you can find in the first post, I added a second table for those (or actually the other way around). This table has further geographical information so you can plot the data on a world map and see where most of Ibn Tulun’s manuscripts or works are held today. The two variables give different results as one manuscript can contain up to more than thirty individual works.
In the meantime, however, the data sets uploaded in the prior posts have become somewhat outdated. I have discovered new manuscripts through archival visits and by simply cleaning up my data set. I also added more consistently information about when manuscript copies were produced and on which topics Ibn Tulun wrote the most works. Since I can be impatient and want to see results for the repetitive work that is data entry, I initially used Palladio again and I like the results. And this post will only deal with Palladio, its allure and shortcomings. Number (2) will then move to Gephi.
The first image shows the most central themes in Ibn Tulun’s corpus. Note in the lower left center the big cluster of hadith related topics, including key words such as hadith, isnad, and so on. The bigger a circle is, the more works it is related to. One issue is, however, that this does not work the other way around: The light circles for works get bigger when you apply more keywords to them. That means that the better you know a work and the more effort you spend on defining it through keywords, the more important it appears in the graph.
In a second step, I looked at the temporal distribution of copies of Ibn Tulun’s works. In order to estimate the diachronic relevance of an author, this is an important step and network visualization can actually be a helpful tool in making rhythms of reception apparent. The second graph shows how works (dark) relate to moments of production (light).
The biggest cluster of works (top right) were produced by 950 AH, shortly before Ibn Tulun’s death. This is an oversimplification; I chose this date for most autographs that cannot be dated otherwise. The only exemptions are works not mentioned in Ibn Tulun’s work list. I assume that they were written even closer to his death in 953 AH.
In the lower half you see proof of an evenly distributed albeit low degree of reproduction between the 16th and 19th century. Finally, the cluster in the top left refers to more concentrated copying activities in the early 20th century. They took place exclusively in Egypt and Syria. There is a visible overlap between these four years as well as with the autograph cluster. It might be interesting to run this visualization again but with decades or centuries instead of individual years. It might also be interesting to include photographic reproductions of manuscripts. It would certainly emphasize the importance of that period for our current evaluation of the author.
Continuing in this line of thought, in what were later readers interested when referring to Ibn Tulun’s works? We cannot automatically assume that reading interests remained the same always and everywhere, unless we’d buy into tropes of an “unchanging Orient”. Let’s not do that.
Instead, one way to get an idea of diachronic developments in reading practices could be to look once more at the topics covered. This time, I related them to the type of manuscript: either autograph or copy.
We can see three clear clusters of topics emerging: those that are only covered in autographs, those covered only in copies, and those covered in both. The graph also gives away something about the number of manuscripts addressing certain topics: obviously, the most important topics can be found in the shared cluster. A next step could now be to identify those copies that deal with topics not found in the autograph or shared cluster. Whereas those clusters illustrate continuations of interest (through reproduction or preservation), this cluster could also show changes. To substantiate this hypothesis, we need to look at the times and places of the production of those copies.
This approach does, however, not account for the reproduction of individual works. For instance, the keyword “biography” to be found in the shared cluster refers to a rather significant part of Ibn Tulun’s corpus—overall some fifty works, some of which rank among the largest he has written (see e.g. here). If we only look at the three largest ones, al-Ghuraf al-ʿaliyya fī tarājim mutaʾakhkhirī al-ḥanafiyya, al-Tamattuʿ bi-l-iqrān bayn tarājim al-shuyūkh wa-l-aqrān, and its Dhayl Dhakhāʾir al-qaṣr fī tarājim nubalāʾ al-ʿaṣr, they show quite divergent patterns of transmission.
The first work, which focuses on Hanafis, was copied only once between the 16th and 17th centuries and is nowadays held in Istanbul (the autograph volumes are held in Cairo and London). It is possible that this copy was deliberately made for the Istanbuli context, whereas the other two works were reproduced in a decidedly Damascene framework. The second one only survives in an excerpt from early 17th-century Damascus (now held in Berlin). The final one exists in a fragmentary autograph and three copies made between the 18th and 20th century (held in Cairo and Gotha), thereby indicating different rhythms of reception. The final work indeed might have provided then influential Damascene families such as the Shuwaykis with social capital.
But I digressed. Let’s get back to Palladio and network visualization more general. Palladio shows itself as an easy tool to play around with your data. I think the graphs above make that clear enough. Yet, it has its limitations. Miriam Posner says that more clearly in her introduction to the topic than I can. Palladio does not allow you to use color schemes for your edges and nodes; it does not show the weight of the edges either. And finally, it gives you no tools for further analysis: which nodes are central in connecting clusters to each other and which have more connections to on another than others?
In order to answer these questions, we have to use a more powerful tool, which could be either Gephi or Cytoscape. But both tools require to enter data in a different way wich reduces the bipartite network described here to a monopartite network. In other words, we have to turn either manuscripts or works from nodes to edge attributes. And that will have to remain for a future post.