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.
