So, how ’bout them networks, huh? They’re a pretty intriguing way to look at data sets, if I do say so myself. Unlike with a regular old chart or graph, networks allow you to explore different connections between “types” of data, such as the example of books and authors Scott Weingart gives in his article, Demystifying Networks. He breaks down the complexities of networks into very simple terms, explaining their benefits through, essentially, relations of stuff. In the case of the books example, books and authors are both “stuff,” and a network would show the relations between them through a fancy display of nodes and edges. And while Weingart warns that networks shouldn’t be used for everything, they can definitely come in handy for a lot of data, particularly sets that only deal with one or two types of “stuff.”
Of course, perhaps my favorite thing about networks is that they’re mobile. You can move around the different nodes, make them bigger or smaller, change their colors, and each new thing you do to them gives you a new perspective on the data you’re networking. Let’s take a look at some, shall we?
Now, the first network I’ve embedded here is from Google Fusion Tables. It was made using a sample data set taken from Palladio, a free site that allows you to create maps and networks at your leisure. The data set displays a list of influential people who went to Monaco at some point in their lives, along with several attributes about them – their dates of birth, death, birthplaces, etc. In my little network here (which you, humble reader, can drag around if you so desire), I’ve chosen to display connections between people’s places of birth and places of death. The colors of the nodes differentiate between the two different types of data – yellow is for birthplace, blue is for death place. And Google Fusion Tables has kindly sized the nodes for me, so it’s easier to see which birth and death places were common among these people (the biggest nodes being the places that correlated the most, like, unsurprisingly, Monaco). I also chose to make the links between nodes directional, giving them cute little arrows so you can get a better sense of how these connections flow. The wider arrows, like the bigger nodes, show a greater amount of connections between nodes than the smaller arrows do.
Moving away from Google Fusion Tables, I have a few networks I created in Palladio as well. These came from a different data set – a spreadsheet that includes the names and relationships of people who helped one another in a historical documentation of the Holocaust. (The data was collected by Marten Durer and can be found here.)
This network up above (though a little blurry, I apologize) shows the web of connections between people in Durer’s list who both gave and received help from each other. I chose to highlight the recipients of help in a darker gray, and to size the nodes so that it was clear just how much help each one received from other people. Rita and Ralph Neumann were obviously the biggest recipients out of everyone, but they were not the only ones; they also gave help to several different people, as the network demonstrates with, say, their middle-ground connection to an Ausweis Nazi. It’s interesting to see the relations of aid that occurred between all these people, because even though some may have never met, or didn’t help each other out, they’re still connected through their helping of someone like Rita or Ralph.
Now, this last network is a little different. Instead of focusing on givers and receivers, I decided to look strictly at receivers, and the types of help they received. Durer organized his data on types of help into a numeric system, hence all the numbers in this network instead of word labels. Though I can’t actually tell you what each number means, it is interesting to see how this kind of network differs from one dealing with two different lists of people. For one thing, the amount of names on this list is a lot smaller. With all the givers gone, it’s easier to see how few people actually got some kind of help out of this data set. The dark gray nodes are still the receivers, but the light gray ones are types of help this time, and they’re much more varied in size than the giver nodes were. This allows us to see what kinds of help were most commonly received. The connections between receivers are established through kinds of help here, instead of through who gave help to them. So while it shows us nothing about human relations, it provides a different view of what actually happened when givers offered help to the recipients. (Who knows what happened with poor Herald up in no-man’s-land there. Apparently he needed his own special brand of help.)
Looking at these different kinds of connections between people reminds me a lot of Kieran Healy’s article, Using Metadata to find Paul Revere. Healy took a simple set of metadata with the names of colonial U.S. people like, of course, Paul Revere, and the different organizations those people were a part of, and used the metadata in order to create various connections between those people. One data set showed how the organizations were connected by how many members each had, how the people were connected by their organizations, and several other snippets of interesting info. By creating a network of the metadata, Healy was able to pinpoint Paul Revere as one of the centers of all this activity, for he was connected to a large number of people and organizations. What we’ve been able to do with Palladio and Google Fusion Tables is similar. Particularly with my Palladio examples, we can see how almost any metadata can be rearranged and viewed from different angles, in order to discover and display different relationships between people, places, and other quantifiable things.