Humanities Intensive Learning + Teaching, Day 5, Maryland Institute for Technology in the Humanities, #hilt2014Posted: August 17, 2014
Sorry for the delay in completing this – it has been crazy and I prefer to write directly into the live blog, which means a network feed, as I just find it easier to put things together this way. (It’s only been a week (ish) anyway.)
Today (well, then), we looked at modularity and how we could break networks into separate communities. This is important because it helps us to be able to see structure. The human eye is a great tool but it often needs help and modularity is a good way to do this. We have strong ties between components in directed networks (unidirectional) because we have taken the time to say that the link goes this way. We have weak ties in undirected networks because there is no solidity to the association form one side to the other. The more connected something is, the more strongly tied it is to the things it connects to so, when we hunt for communities, we want to take away the least number of connections to produce the largest number of connected communities.
Let’s look at the Hamlet graph (after Moretti) with some colouring added for the various communities and all the connections left in.
Now let’s take out the intra-community links.
Now we can suddenly see, in terms of dialogue exchanges and interaction, how isolated Hamlet actually is. The members of the court are one community. The invaders are another. Horatio, the proxy for the Danish state, is deeply involved in statehood. Hamlet, however, is more heavily connected to the characters who help with his dawning realisation that something has gone awry. No wonder he goes mad, he’s the Nigel Nofriends of Medieval Denmark, with only the insane Clown Posse and a group of second-rate actors to keep him company.
After this we spent some extensive time working on producing prettier looking graphs with Gephi and using more advanced algorithms to help us to represent what’s going on. We looked at the Twitter data for the conference and came up with this:
This shows the separate groups of Twitter user communities who used the HILT2014 hashtag. My small cluster is the small purple arrow bottomish left. We’ve also ranked the size of the data labels based on the significance of that person in the Twitter information chain, using the PageRank algorithm. So, Jim McGrath wins Twitter for HILT 2014! We also look and how important certain links were in the network by looking at edge betweenness to determine which links are used by the most shortest paths and then use this to identify important connected components.
Lots of good hands-on stuff and a very interesting course! I learned a great deal and have already started using it elsewhere.
Humanities Intensive Learning + Teaching, Day 4, Maryland Institute for Technology in the Humanities, #hilt2014Posted: August 8, 2014
Or, the alternative title, “The Play Formerly Known as Hamlet”. Today had a lot of fascination discussion where the bipartite nature of our class network became apparent, in terms of the the majority of the class who were Digital Humanists and really understood a detailed close reading of Hamlet – and your humble narrator.
Today we talked about modularity, which allows you to break your network up into separate modules, which makes a lot more sense when you call those modules things like communities, groups or clusters. Can your network be split up into areas where some groups are more connected to each other than they are to adjacent nodes? By doing this, we are trying to expose structural features of the network and, in particular, unexpected aspects of network structure – is there some set of nodes that have a role in the network that we don’t understand? If we talk about this in terms of people, if our social network is made up of small communities with connections between the communities, then we would expect gossip to spread more rapidly inside a community than between the communities. Simple, eh? The approach we take depends upon comparing the structure we have with a model of a random network using the same number of links.
Once we’ve done this, we can use this in a tool, such as Gephi, to clearly illustrate the groups. Here’s a grumpy Dane in illustration.
I’ve coloured and tagged the network to show the key players in Hamlet, based on Moretti’s analysis of Hamlet, which attached unweighted connections between participants who had direct conversations. Now, we’ve made the size of the nodes reflect how relatively important (in terms of betweenness, the number of paths that must go through this person). If we did this in terms of PageRank, another measure of the relative importance of nodes, based on their connectivity, the nodes in green would jump up in size. But what you should note is that Hamlet and Horatio are roughly the same size and, while Hamlet is much more connected to everyone (quelle surprise, given the play’s named after him), the only thing that we lose if Hamlet disappears is that we no longer can hear from the Insane Clown Posse, Lucianus and the Lord. In purely connected terms he doesn’t appear to be that important. We’d obviously lose a lot of the text if he disappeared but how important is Hamlet in Hamlet?
This led to a lot of discussion in class as to the validity of the original Moretti pamphlet, much of which is core to the entire discussion of Digital Humanities. How valid is any conclusion from a network model such as this when the innate nature of the capture process (to provide the capta) may completely remove the ability to draw certain conclusions? The class discussed the impact on perceived and invisible observers, who strictly don’t have dialogue with each other but potentially have an impact upon other characters and their narrative interactions. (The Moretti pamphlet may be found here.) Moretti had a lot to say about Horatio as a proxy for the state and was very interested in the closeness of Hamlet to everyone else, but (later on) we ran some random network experiments and it turned out to be the type of connections in the network (the clustering coefficient) that was more interesting than the closeness.
We then moved on to a discussion of a number of useful metric for networks, including the clustering effects that tend to indicate intention in the system we’re studying (there’s no real reason for a random network to form a small number of unique clusters unless you tune for it.) We also discussed the Small Worlds of Watts and Strogatz (1998) where you have cliques of nodes (tightly connected clusters) linked together by a smaller number of links, characterised by a power law distribution and a higher clustering coefficient (very basically).
We generated some random graphs to see if we got the structure we saw in Hamlet as noted earlier. Why? Because looking at Hamlet and drawing out information from the structure only has validity if (1) the model is accurate and (2) the situation couldn’t have easily arisen by chance. As noted, we generated a random graph for the same number of nodes and it had a similar average path length and identical diameter – but very different clustering coefficients! So it turns out that Hamlet wasn’t written by a monkey after all.
The final part of the session was on dynamic networks. This is the study of networks that change over time and we worked with some data that showed people’s association with an area over time. We could look at this in terms of change in time or in terms of progress through a corpus (chapters are as useful as dates here). What you want is a start date and an end date for the feautres in your network – when should the feature be there and when should it not be there anymore? It turns out that Gephi is quite convenient here, because you can merge a start and end time and end up with a time interval. Not bad, Gephi, not bad. Now we can see when things are active – great if you’re looking to see when students are active in forums or activities, for example. Here’s an example of the difference in the test network between the first and second halves of 2009, with all nodes of zero degree (nodes with no links) removed.
We then played around a lot with a vide variety of animations over time, including ongoing calculations, colour and shape alterations. Basically, it was rather hallucinatory by the end but that may be the Dr Pepper talking. We even got to see the sparklines (Zelchenko/Tufte) for some extra visualisation goodness!
This is one of those classic separations between the things we visualise for ourselves to help us to work out what’s interesting about a dataset and what we would visualise for presentation, especially to non-discipline people. There’s a really big difference between what scientists want to see and what other people want to see – and if we don’t realise that, then we risk either having presentations that don’t communicate enough information or we kill people with minutiae. Look at this – how useful is it for you?
Another good day but I think there are some very full brains!