Many networks, from the Internet to Facebook, are transitive: neighbors of the same node are probably neighbors of each other, or in social terms, your friends are likely to be friends with each other too. Apart from a few special cases, mathematically modeling such clustered networks is difficult and calculating their properties almost always requires numerical rather than analytical solutions. But as Mark Newman of the University of Michigan, US, reports in Physical Review Letters, it is in fact possible to generalize random graph models to include clustering in a way that allows exact derivations of network behavior.
Luís F. Simões on 21 Mar 11a nice review on the wonders of Hierarchical Bayesian models. It cites a paper on probabilistic programming languages that might be relevant given our recent discussions. At Hippo's farewell lunch there was a discussion on how kids are able to learn something as complex as language from a limited amount of observations, while Machine Learning algorithms no matter how many millions of instances you throw at them, don't learn beyond some point. If that subject interested you, you might like this paper.
Ma Ru on 21 Mar 11Had an opportunity to listen to JBT and TLG during one summer school.. if they're half as good in writing as they are in speaking, should be a decent read...
