Category Archives: visualization
After trying to map the French version of Wikipedia a couple of days ago, I’ve played around with the much bigger English version (the dbpedia file I worked with contains 130M links between Wikipedia pages in a cool 20GB) this week-end and thanks to a rare lucid moment I was able to transform that thing into a .gdf that is small enough to be opened in gephi. I settled for the 45K pages with the most links (undirected) and started mapping. All three maps I built use the OpenOrd layout algorithm (1000 iterations). The first uses the modularity measure for “community” detection and colors text accordingly (click on the image for a very large version):
The second uses a grey color scale to express the degree (number of links) of a page:
Finally, the same map, but with a different color scale (light blue => yellow => red):
Every version helps with certain readability issues and you can download all tree of the maps as a big .psd so you can easily switch between the different modes.
When comparing these maps with their French counterpart, there are several things than are quite remarkable:
- Most importantly, there is no cluster that I would qualify as “common culture” or “shared knowledge”. There is most certainly a large, dense zone at the center but while the French one draws in all kinds of topics, this version has worldwide country information only. I would prudently argue that the English version of Wikipedia shows a more globalized picture of the world, even if there is a large zone of pages on the left that deals with the United States. It’s a bigger and more heterogeneous world that emerges, but there still is a dominant player.
- Sports is even bigger on the English version and typically American sports (Baseball, NASCAR, etc.) show up on the left in smaller, denser clusters compared to the gigantic football (soccer) area on the center to bottom right.
- The Sciences are smaller but entertainment (TV, popular music, comic books, video games, etc.) is much more present. At least at this level of observation.
- There are some seriously “strange” clusters, such as the dense yellow zone on the far right halfway between top and center that shows a group of Russian painters I have never heard of. Not that I’m an expert but I’ve found little trace of any other painters. This shows the weakness of my selection method by link degree – if there was a way to select nodes by page-views, the results would probably be very different, at least for our Russian painters. But it also shows that despite having become a rather respectable Encyclopedia with a quite classic subject outlook, Wikipedia still is a space for off-the-track topics and for communities that are so passionate about a certain subject that they will groom it and grow it.
I plan on releasing the scripts used to build these maps in the future but I want to try out a couple more things before that, most particularly a version that only takes into account in-links, which should reduce the presence of certain “distributor” pages (“events in 2010″,”people alive”, etc.).
Edit: a map of the English Wikipedia is here.
Wikipedia is a fascinating object for way too many reasons. The way it is produced, the place it has taken in society, it’s size and evolution, and many other aspects are truly remarkable. Studying Wikipedia has become a discipline in itself and while there may be certain signs of fatigue on the editing front, there is still much to learn and to discover. I have recently started to take an interest in looking at the way knowledge is structured in different contexts and the availability of certain tools and datasets makes Wikipedia a perfect object for scrutiny. If it just wasn’t that big. Still, it’s the 21st century and computers are getting really fast, so why not try mapping Wikipedia. All of it.
There are different ways to start such a project, but simply taking the link structure is probably the most obvious. This allows for bypassing the internal taxonomy and may lead to a more “organic” expression of underlying knowledge structures. Unfortunately, computers are not that fast – at least not mine – and so I had to make two concessions: I took a non English variant (I settled for French) and reduced the number of nodes to a (barely) manageable amount. The final graph file (.gdf – do not even think about working with it with less than 4GB of RAM) was built by taking pages that had at least 100 connections with other pages. From an initial 183K pages and 11.5M links I went down to a more manageable 40K and 2M respectively. To make things workable, I chose to visualize the page names only, no nodes, no edges. The result looks like this (click on the image for a very big .png):
Reliable gephi did not only do the graph layout (OpenOrd plugin, 1000 iterations) but dutifully detected “communities” in the network, which actually did work really well. And here is a version in elegant grayscale, this time without community detection:
The graph shows a big dense zone in the middle that is quite unreadable but composed out of world history, politics, geography, and other elements that constitute a core set of knowledge elements that are highly interlinked. While France plays and important role here, these elements are actually very globalized and include countries from all over the world. Could we interpret this as a field of “common” or “shared” knowledge? A set of topics that transcend specialization and form the very core of what our culture considers essential?
To the close right of the very center, there is a rather visible (in orange) cluster on the United States. Around the center you’ll find major historic events and periods (WWII, middle ages, renaissance, etc.). The arts are on the right (mostly music) and France’s most popular art form – Cinema – starts at the top right, in a highly dense orange cluster and goes to the top left, tellingly fusing with theatre. The Sciences form a rather strange blue band the goes from the center top to the top right.
And then there is sports. I was a bit surprised by how much of it there is and how well the clustering and community detection works for identifying individual fields – football, tennis, car racing, and so on. The second surprise was how few “geek” subjects appear on the map. There is a digital technology cluster on the top right but I haven’t found any traces of the legendary Star Trek cluster. In the end, French Wikipedia appears to be a rather classic encyclopedia if you look at it from a subject angle. Could we use such maps to compare subject prominence between cultures?
Obviously, the method for mapping Wikipedia has to be refined to make maps more readable but the results are actually already quite telling. Let’s see whether the same approach can work for the English version – which is a cool 10 times bigger…
The digital methods initiative at the University of Amsterdam – incidentally my new employer – has an ever growing list of very useful tools that help with studying online phenomena. The Wikipedia Network Analysis tool (like most DMI software written by Eric Borra) is particularly interesting if we simply take into account the place of Wikipedia in our contemporary knowledge configurations. The tool crawls Wikipedia from a starting URL (by default at a +2 radius) and – amongst other things – spits out a source node / target node list of links between the different pages.
To visualize the data, you can use Many Eyes but there are significant limits to woking with online tools. This little script will take the source/target data and create a gdf file you can explore with gephi or guess. This is a Wikipedia network surrounding the page on data visualization:
What is rather incredible is that I actually filtered the nodes with only one connection from the graph, going from 4995 to 690. Wikipedia is has become big. Very, very big.
An interesting insight to take from this graph is that many of the data visualization pioneers are placed at the center of the network, indicating that the field has grown and diversified from a limited set of initial concepts and experiments – something that can be easily confirmed by looking at the literature of the field where the same examples pop up regularly.
A visualization approach may be interesting for studying Wikipedia as a knowledge platform instead of a social experiment. While the attention given to forms of governance, contribution, etc. is certainly justified, we may want to take a closer look of the actual organization of knowledge on Wikipedia and how this compares to other forms of collecting knowledge.