Humanities Intensive Learning + Teaching, Day 3, Maryland Institute for Technology in the Humanities, #hilt2014Posted: August 8, 2014
Today was going to be a short day because we had the afternoon off to go and do cultural things. (I took the afternoon to write papers and catch up on work. I tend to work in both timezones when travelling because otherwise work will eat my head.) Today we explored a lot of filtering in Gephi, which was … interesting and best done in practice. Which we did. So, hooray!
We looked at Multimodal Network Projection throughout the day but I’ll come back to that. We started looking at other ways of determining the important and dependant nodes in a network, starting with the HITS algorithm, which identifies Hubs and Authorities in your network.
Then we moved into the wonderful world of PageRank, Citation networks and how all of these things work. PageRank is, fundamentally, how Google works out which pages to give you. You won’t be able to find out the details of the way that Google runs PageRank because gaming PageRank to serve up pages that you probably didn’t want to see is very big business and highly irritating. Search Engine Optimisers (SEOs) spend a lot of time trying to reverse engineer the algorithm and Google spends a lot of time tweaking it. It’s good we’ve solved all the important problems in the world so we can spend time on this.
Back to PageRank. PageRank looks at the number of links going to a node and what the quality of these links is in order to work out which the most important nodes (pages in the case of Google search) are and, hence, which ones you want. In Digital Humanities, you can use this to identify the most significant element of your model – which, by the way, quickly establishes that Hamlet is not as important as you think. Take that, Hamlet! Want more detail on PageRank? Look here.
In Citations, we want to see how is citing which reference, which is straightforward. In Co-Citation networks, we want to measure how often two documents are cited together. There are many reasons for looking at this, but it helps to detect things like cronyism (someone citing a paper because they like someone rather than because the information is useful). As we discussed before, the Matthew Effect comes in quickly, where frequently cited papers get even more frequently cited because they must be good because they’re cited so frequently. (Tadahhh.)
We also looked at a rather complicated area of multimodal projection, which is going to need some set-up. If you have a set of authors and a set of publications, then you can associate authors with publications and vice versa. However, this means that the only way for two authors to be connected is by sharing a publication and similarly for two publications sharing an author. This is a bipartite network and is very common in this kind of modelling. Now, if we make it more complicated, by moving to a conference and having Authors, Papers and Presentation Sessions, we now have a tripartite network and this becomes very hard to visualise.
What we can do is clean up this network to make it easier to represent by hiding some of the complexity in the connections between nodes. Let’s say we want to look at Authors and Presentation Sessions. Then, while the real network is Authors connected to Papers connected to Presentation Sessions, we can hide the Papers with a network link that effectively says “connects this author via a presentation to this session” and suddenly our network looks like it’s only Authors and Sessions. This level of visual de-cluttering, which is dimensional reduction for those playing along at home, makes it easier for us to visually represent the key information and produce network statistics on these simpler graphs. It’s also a natural fit for triple-based representations like the Resource Description Framework (RDF) because the links in the network now map straight to predicates. (Don’t worry if you didn’t get that last bit, some people just got very excited.)
Finally, we looked at how we collect information. Is it stuff we just pick up from the environment (data) or is it something that we choose the way that we collect it (capta)? (Capta comes from the word for capture. Data is passive. Capta is active. Take that, Bembridge Scholars!) If you think about it, every time you put your data into a spreadsheet, you are imposing a structure upon it, even down to which column is which – it’s not technically data, it’s capta because your interpretation alters it before it even reaches the analysis stage. When it comes to the network that you draw, do you care about the Proximities of elements in your network (location, membership or attitude), the Relations in your network (role, affective, perceptual), the Interactions or the Flows? All of these are going to change what the nodes and edges (links) represent in the network.
The simple rule is that entities are connected by relationships (For those who think in tuples, think “subject, predicate, object” and get your predicate on!) However you do it, you have to pick what’s important in your data, find it, capture it, analyse it and present it in a way that either shows you cool things or supports the cool things that you already know.
A lot to cover today!
After the session, I did some work and then headed off for dinner and board games with some of the other people from the workshop. A nice relaxing night after a rather intense three days.