As it is something I don’t know a whole lot about, I recently got the bright idea to to start working with social network analysis in infection transmission. A search of the literature turned up a few interesting gems, mainly of infection transmission through sexual networks, but little in the way of actual data. There were plenty of boiled down examples of other people’s data, but they don’t post the data for people to play with. I could easily simulate some data. A network analysis software package, UCINET, has a feature to create a random network. However, I felt this to be cheating and desired to get my hands on something real.
In a rare moment of spontaneity, I posted a call for study subjects through the School of Public Health’s open student mailing list. Surprisingly, I got about 65 responses of people willing to expose their contact networks for a day. Picking a single day, I asked the group of volunteers to fill out a form stating whether they had contact any of the other volunteers on the list and how many people they may have had contact with who were not on the list. I was intrigued by how many people were willing to answer the survey and return them to me with out compensation of any kind. I was also surprised at how difficult it is to create a survey that provides you with exactly the type of data you want in the format you wish.
The basic network of people who were on the list looks like this:
At first I was worried that the data might be worthless, due to the lack of overlap in volunteers or possibly due to too much overlap, as might be the case if all of the people on the list have a class together on the study day. However, the network appears intuitive, and knowing the individuals on the list, the clustering present is logical. The circles in the top corner are isolates who had no contact with people in the group. The red dot in the center is me. I had a wide variety of contacts since I was the one doing the survey. Although scientifically, it might not make sense to include my self in the contact network, I do have contact with many of the people on the list regularly, so I could as a member. The clump to the left of me is primarily Epid PhD students, of which I’m a member. It has to be said that they provided the most concise data.
Including the contacts people had that were not on the list, we can see that the results get a little more interesting:
In addition to the cool looking patterns, we can see that many people have contacts well outside the immediate study group. In fact, the people in this study had a mean of 20 contacts per person for the single day. The contact distribution was highly skewed, with some people having as high as 150 contacts for the single day. Contact rates varied by department and by degree. The colored circles represent what’s called “K-cores”, that is groups that are more connected to one another than with other groups. Here, in this case, the K-cores roughly turn out to represent differernt departments represented by the study group. In fact, it’s is fairly surprising how well it pegged individuals into their respective groups. It even positioned me right between Epid and Biostat. I am one of the larger blue dots up top, and Epid (blue) and Biostat (green) are to the left and right of me. HBHE is mostly scattered black dots. The size of the dot represents the relative proportion that the member constitutes of the K-core.
Most of the study group were folks from the School of Public Health. HBHE is by far the most connected of all the departments. Other (a mixture of departments spread around campus) was the least connected of the people who bothered to report their affiliation. Not surprisingly, PhD students reported a lower level of contact than Master’s students, a difference confirmed by a Wilcoxon test with a p-value of .033.
I was sick all last week with Norwalk. I never realized how absolutely liberating it is to not be hungry, thirsty or desire anything at all physically. I could eat a single bowl of soup and be completely content. Now, I’m back to health and starving and wasting my time eating and foraging for food. I think that the biggest hurdle to our intellectual progress is the continual waste devoted to satisfying our physical needs. It’s really not that I hate food or anything else, it’s that I just simply don’t have time for it. Other people live for it, but they really must hate what they do. I can’t wait to be 86.
The British, on the other hand, have come up with a brilliant strategy: create a cuisine so repulsive that you simply never desire food.
In other news, looking over this blog, I realize what an entirely boring person I am. Other people post fun pictures of themselves socializing, drinking with friends, hanging out with their kids and family, I write posts about discovering new sources of SCOTUS data, and implementations of models of segregation behavior. Other people are posting about newly discovered records and freaky keyboard metal bands, I am posting about Obama’s budget as represented in the New York Times. What the fuck happened to me? I think I was always this way, but grad school has exacerbated the old fogey in me.
Here is Ann Arbor this morning:
Personally, I think this NYT feature on the new budget is pretty great. I’m sure the fleabaggers would like to get rid of everything but the top left corner. Guess we’ll have to depend on local government to provide educational funds, unemployment benefits and highway funds. I’ve never understood the right-wing infatuation with local governments. I can’t think of a more inefficient hotbed of corruption. My local property taxes cost more than all of my other taxes combined (including sales taxes) and I literally get zero in return and almost zero voting power.
I just found the greatest website, the Supreme Court Database (http://scdb.wustl.edu/about.php), a stockhouse of every SCOTUS decision since 1953, all organized in a coherent form for quick digestion.
Screwing around with the data for five minutes, I found that there have been no SCOTUS decisions since 1953 that had less than 3 yea votes and only ONE that had 3. Most decisions have between 5 and 9 votes, indicating that decisons are often split down the center or that the court most often agrees. In fact, there are more unanimous decisions than otherwise, as the set of box plots below indicate:
The single case with only 3 votes was that of Abraham J. Isserman, who the New Jersey Supreme Court had disbarred due to his willingness to defend the leaders of the American Communist Party in 1949, a case which lead to the imprisonment of the leadership. Isserman protested the result and accused the court of pandering to the prosecution and judicial misconduct. This resulted in Mr. Isserman being found in contempt and disbarred. He then spent 4 months in prison. The case rolled it’s way up to the SCOTUS, and with only 3 votes, Mr. Isserman was found to be unjustly disbarred and afterward had his license to practice law in the State of New Jersey reinstated.
An obit of Mr. Isserman is here, he died at 88 years on age in 1988.
One thing I have discovered is that the SCOTUS’ workload has decreased since the 1980s and they tend to unanimously agree much more than they used to. Basically, this implies that they have somehow streamlined the process which decides which cases get sent to court for the full vote, rather than accepting every case that comes by.
Also, since writing this post, I have discovered that the SCOTUS agrees to hear cases per the Rule of Four. Each justice has a legal team tasked to weed through the 7000+ petitions to the Court every year. If the legal team finds a case possibly worthy of being heard, it is referred to the Justice and he is allowed to pitch it to the other Justices. If four justices can agree to hear the case, then the case is heard.
This leads me to question, if four votes are required to hear the case, then the hearing itself is merely a formality to convince at least one other judge to vote with the four who allowed the case to be presented. In fact, most (over 90%) cases that are eventually brought to the full court are ultimately passed. So, what is the point of even having 9 judges, if they are going to mostly unanimously vote in favor of the case. More importantly, where is the decision power in the case that 1 or more judges dissent? Is this dissent in name only? It reminds of the Republican minority in the Senate. You can be as contrary as you like when you have no voting power.
I need to get back to work.
Grad school is marked by long periods of isolation and silence. To make up for a lack of social life (and skills), I, like many others, resort to creating semblances of social circles through social networking sites like Facebook. In doing research for a paper I’m working on, I am reading up on network theory and happened upon some software that allows me to export my friends list from FB with all of the internal connections between them. UICNET (http://www.analytictech.com/ucinet/) is a small but powerful program designed to perform social network analysis. Basically, it takes all the connections between people you know and is able to draw them for display using a number of criteria. The raw list read into it ends of looking like this:
I have approximately 300 friends, most of which I actually know. Using a neutral criteria for display, I can see distinct groups from various points of my life. There’s an EMU group in the bottom right corner, a University of Michigan Group on the left, and a huge clump of people that are mostly music related, i.e. pre-grad school. Within the pre-grad school group, I can see distinct clouds of varying time points, Boston, Noise related folks, Mississippi folks and some others. There are some isolates, basically people I know from various disconnected events such as the time I spent in Germany and students of mine from JCC, among others. I find it interesting that Joe Kacemi is my bridge from UM to my cloudy music world. Thom Klepach is my link from EMU to my music cloud. Without Thom and Joe, there would not be a single link between pre-grad school music world and my grad school life.
Using the software to isolate 6 specific “Factions” within the entire list I am able to produce this:
Now, it’s much more clear. There is one group of complete isolates, a random bag that combines Boston and EMU, some odd group consisting of people that I rarely talk to, a Mississippi group, a University of Michigan group, a collection of msuic related people that I knew from Boston and San Francisco (basically, 99-2002) and a group of Michigan music people. Mostly, it makes sense in the grand scheme of my life. The amalgam of Boston, EMU and Canada is quite strange, however. I think that the level of connections between the major music-related groups is fascinating. The program divides them up into two distinct factions, despite the large number of connections between the two.
Finally, I did an eignevalue analysis and got this one:
The only reason I present this is because, oddly, all three members of Wolf Eyes appear to form the base of the organized list. I’m not sure why. Basically, according to this, all of my social relationships start with Aaron Dilloway.