First, some general advice about reading technical papers! Read the introduction, then the discussion/conclusion, then the experimental results, and THEN the technical details (in this case, the section entitled "Inference of rank from network structure"). This is out of order, obviously, but it's a good triage strategy: you read things in increasing level of difficulty, so that you always know whether you care enough to read the next, more difficult part. It's also good because you have more context to interpret the difficult parts if you've read all the easier parts of the paper.
I've put a list of wikipedia articles at the bottom of this email, under the heading "Study Guide". They are a hopefully comprehensible explanation of the math needed to understand the most technical parts of the paper. But, if you don't feel like wading through the most technical parts, you don't have to read the study guide articles in order to participate in the discussion. (Also, I'm omitting some background for technical parts that I don't know much about, like ANOVA and statistical tests of model fit. If someone feels like adding anything to the study guide, go for it.)
== Paper ==
PDF link: http://arxiv.org/pdf/1205.6822v1.pdf Arxiv page: http://arxiv.org/abs/1205.6822
== Abstract ==
In empirical studies of friendship networks participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower-ranked individual claiming friendship with a higher-ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.
== Code ==
Mike Bishop has some C++ code that (I think?) the authors of the paper are releasing publicly. Mike, can you send that out? It would probably be a good base to start from if we want to run simulations.
== Study guide ==
These are just to be helpful, don't feel like you have to read any of them to participate. And feel free to suggest additions, or say that one of these articles wasn't very helpful.
Adjacency matrix: (http://en.wikipedia.org/wiki/Adjacency_matrix) This is useful because it gives you a dictionary between graph theory and linear algebra. It only comes up a little bit, but it's often used without any explanation.
Elementary Bayesian stuff: (http://en.wikipedia.org/wiki/Bayesian_statistics) This is a very high-level article that gives links to more in-depth articles.
Less elementary Bayesian stuff: (http://en.wikipedia.org/wiki/Bayesian_inference) This is the main piece of background for the technical section of this paper. If you really want to know this stuff, Bishop's "Pattern Recognition and Machine Learning" is the best textbook I know, although it's not accessible without a lot of mathematical background or a lot of free time.
Bayesian games: (http://en.wikipedia.org/wiki/Bayesian_game) I've never seen this before, but it seems like the appropriate math to use when thinking about social science stuff with Bayesian methods.
EM: (http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm) The workhorse of mathematically rigorous Bayesian statistics. It's a flaky piece of shit, but it's pretty much all we have.
MCMC: (http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) The workhorse of mathematically rigorous frequentist statistics.
Gibbs sampling: (http://en.wikipedia.org/wiki/Gibbs_sampling) This is the most mature mathematics that specifies how emergent properties emerge from local interactions. It was first created to model thermodynamics, which is basically all about emergent properties of molecule interactions, like temperature, pressure, etc.
Ising model: (http://en.wikipedia.org/wiki/Ising_model) This is the most straightforward example of a Gibbs distribution, it's what you should think about when you try to understand them.
On Wed, Oct 3, 2012 at 10:37 AM, Eric Purdy epurdy@uchicago.edu wrote:
I am starting a new listhost, math@moomers.org. I am hoping that it will function much like readings, but that we can have long and intricate technical discussions without annoying anyone.
Some proposed ground rules:
- There are no stupid questions, and we're interested in everyone's
opinion. In particular, you are encouraged to subscribe to math@moomers.org even if you do not have much mathematical training. If you're interested in watching math being done, that's all that matters.
- There will be no status games, and no arguments from authority.
Nobody should sign up to math@moomers.org because they want to impress anyone, and nobody can cite their professional status or mathematical training to make someone else shut up.
- If any publishable research is done on math@moomers.org, we will
decide by consensus whose names should be on any papers that result.
The first proposed paper to read is "Friendship networks and social status", by Ball and Newman. It is available online at http://arxiv.org/abs/1205.6822 . I am interested in it because it gives a nice model of social stratification as an emergent property of social networks. I think it would be cool to try to extend it, and I think it should be possible to give a scientific account of the phenomenon of discursive privilege.
Here is the abstract:
In empirical studies of friendship networks participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower-ranked individual claiming friendship with a higher-ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.
-- -Eric