Gerrymandering Fonts

If the exact intersection of your interests is graphic design and a deeply abiding hatred of partisan gerrymandering, then have I got the font for you: pic.twitter.com/w1j6iQvANh

— Morgan Baskin (@mhbaskin) August 1, 2019

Complex Analysis, Simple Analysis, and Congressional Support for Perverse Sheaves

This quote encapsulates one of the great stories in the history of U.S. government funding (and legislative and others' skepticism of such funding):

"On April 9, 1975, Congressman Robert Michel brandished a list of new NSF grants on the floor of the House of Representatives and selected a few that he thought might represent a waste of the taxpayers’ money. One of them (on which I happened to be one of the investigators) was called “Studies in Complex Analysis.” Michel’s comment was, “ ‘Simple Analysis’ would, hopefully, be cheaper.” I shudder to think of what might happen if certain members of the current Congress discover that the NSF is supporting research on perverse sheaves."

You can see some more details in an old blog post from John Baez.

Super Geniuses :)

This is one of the most brilliant political cartoons of all time. pic.twitter.com/6FG4Wsg5pb

— David Moser (@david__moser) July 28, 2017

(Tip of the cap to Melanie Mitchell.)

Beautiful Visualization of the Time Evolution of Ideology in the United States Congress

This visualization of the time evolution of ideology in the United States Congress, which constitutes today's xckd, is absolutely gorgeous.

(It reminds me of a visualization that Jim Moody and my collaborator Peter Mucha are publishing in the journal "Network Science". I don't think they have posted that one on a website yet. It also reminds me of Martin Rosvall's "alluvial diagrams".)

The calculations of political ideology were done using DW-Nominate, by the way. I am biased, but I happen to know of a better method to do this. :)

Soap makes you clean. SOPA makes you dirty. :)

And if anybody else came up with this, be it known that I arrived at it independently.

(As if I would have any other stance on this...)

Oh, and don't forget to sign Google's petition!

"Congress Vanishes into Infinitely Recursive Loop"

According to a new article in The Economist, the United States Congress has managed to get itself into an infinitely recursive loop with its budget follies.

I once crashed all of UGCS when I accidentally started an infinitely recursive loop (it was taking a while, so I lost patience and decided to use the bathroom, and the whole server went down while I was gone), so we'll see what crashes this time.

(Tip of the cap to Puck Rombach.)

One Less Non-Evil Republican

Arlen Specter, one of the few remaining reasonable Republicans, has now defected to the Democrats. Part of me says 'Welcome!' and part of me feels a bit bittersweet that that party has one less beacon of hope in it. (Of course, one can easily see where Specter was in practice based on the voting and legislation cosponsorship records. In fact, my collaborators and I have a couple of plots in our papers that convey it reasonably well.) On the other hand, there is something to be said for being filibuster-proof...

At least he did well in roompicks

Courtesy CNN.com, one of the Freshman Congressman is sleeping on a cot in his office when he is inside the Beltway. Nice!

Podcast version of my 'Unearthing Power Lines' Mathematical Moment

The podcast version of the "Unearthing Power Lines" Mathematical Moment based on my research on Congressional networks was posted on the American Mathematical Society website today. The interview took place during the Joint Mathematics Meetings in San Diego last month. The interviewer was Mike Breen of the AMS.

I really am in the mood to guest-host Berkeley Groks again. That was fun. Also, tomorrow is the ten-year anniversary of a particularly memorable episode of the old radio show that Lemming and I used to have. I might blog about this tomorrow. There's always room for a black celebration. :)

"Community structure in Congressional cosponsorship networks"

Another of my papers on communities in Congressional networks just came out in Physica A. This one arose out of a SURF project I sponsored in 2006, and the student in question (Yan Zhang) is the first author of the paper. The next two authors, A. J. Friend and Mandi Traud, are also undergrads. Then we have all the non-students: Me, political scientist James Fowler (best known for a recent well-publicized paper on the spread of obesity through a social network), and applied mathematician Peter Mucha.

The abstract for the paper reads as follows:

We study the United States Congress by constructing networks between Members of Congress based on the legislation that
they cosponsor. Using the concept of modularity, we identify the community structure of Congressmen, who are connected via
sponsorship/cosponsorship of the same legislation. This analysis yields an explicit and conceptually clear measure of political
polarization, demonstrating a sharp increase in partisan polarization which preceded and then culminated in the 104th Congress
(1995–1996), when Republicans took control of both chambers of Congress. Although polarization has since waned in the U.S.
Senate, it remains at historically high levels in the House of Representatives.


Ye Pei's SURF project from 2007 followed up on this work and looked at political realignments using voting networks. Essentially, we have found that a particular graph-theoretic concept called "modularity" (which looks at the connections inside a group of nodes in a subset of a graph versus connections between nodes in different subsets) can be used to give a nice measure of political partisanship. Realignments occur when the best splitting gives a much higher modularity than the splits one obtains by dividing the graph purely by party identification. We are going to start writing up a research paper on this stuff pretty soon, but in the meantime, you can take a look at Ye's SURF report.

Interesting new paper (on topics near and dear to my heart)

Here is a really interesting new paper that just got posted to the arxiv.

It discusses a very interest way of constructing datasets for social networks that is very appealing and I would like to use in future research. (In fact, even though I haven't had the chance to read it closely yet --- I started rectifying that earlier this evening, though I don't plan to finish reading it until tomorrow --- this paper has already given me some new ideas for undergraduate and/or Masters research projects for this summer.) Moreover, the working examples in this paper cover three of my favorite groups of people: physicists, U.S. Senators, and baseball players. (It's like the authors are trying to steal my heart: not only did they do something really interesting with real-world networks, but look at the examples they chose! And they even cited one of my papers. If I ever decide to deal with children, I think I'll simply have to adopt some of the authors of this paper.) Also, I very much appreciate the use of the word "googling" in the paper's title. I use the word a lot (and I know others among my crowd at least use it occasionally), but I had never previously seen it in the title of a scientific paper.

Here is the paper's abstract:

Recently, massive digital records have made it possible to analyze a huge amount of data in social sciences such as social network theory. We investigate social networks between people by extracting information on the World Wide Web. Using famous search engines such as Google, we quantify relatedness between two people as the number of Web pages including both of their names and construct weighted social relatedness networks. The weight and strength distributions are found to be quite broad. A class of measure called the R{\'e}nyi disparity, characterizing the homogeneity of weight distribution for each node, is presented. We introduce the maximum relatedness subnetwork, which extracts the most essential relation for each individual. We analyze the members of the 109th United States Senate as an example and demonstrate that the methods of construction and analysis are applicable to various other social groups and weighted networks.


Their idea for gathering data is awesome! Because I haven't read the paper closely yet, I'll reserve comments as to the analysis they do with that data. However, their choice of methodoly has already inspired several ideas in my head, so that alone makes this paper's existence extremely worthwhile in my book.

The most straightforward of my ideas is simply to use their method of data collection to construct other networks that interest me (such as collaborations among mathematicians and various kinds of collaborative connections in the U.K. parliament). However, I'm also wondering if I can come up with some sort of variant involving Google Battle. (For the record, Oxford beats Cambridge according to this index. Sadly, Caltech doesn't fair nearly as well against MIT. In fact, this latter battle was downright embarrassing for the home team.)

Community Structure in the U.S. House of Representatives

My winning entry in the 2006 Nonlinear Science Gallery of Images was published today in Chaos. This appeared in poster form at the 2006 APS March Meeting. My coauthors are A. J. Friend (an undergraduate at Georgia Tech), Peter Mucha, and Mark Newman.

The Nonlinear Science Gallery, in its third year, was inspired by the Gallery of Fluid Mechanics, which has been around for quite a while. In each of the last two years, the stuff in the gallery constituted the most downloaded papers in Chaos, so besides the value of the short article itself (which is basically an extended abstract, so I'm not going to describe it here), this should do wonders for the exposure of this research project. (The project is already reasonably well-known, but every little bit helps.)

My collaborators and I are currently working on doing some revisions of our archival paper before we resubmit it. We are also working on a follow-up paper that uses the work of one of my SURF students as a basis. (Right now, he's doing a couple extra calculations to quantify his findings. A 0th draft of this paper currently exists, but I'm not sure when we're going to have something ready to submit for publication.)