This article proposes a surprisingly simple framework for the random generation of combinatorial configurations based on what we call Boltzmann models. The idea is to perform random generation of possibly complex structured objects by placing an appropriate measure spread over the whole of a combinatorial class-an object receives a probability essentially proportional to an exponential of its size. As demonstrated here, the resulting algorithms based on real-arithmetic operations often operate in linear time. They can be implemented easily, be analysed mathematically with great precision, and, when suitably tuned, tend to be very efficient in practice.

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a LaBRI, Domaine universitaire 351, cours de la libération, AbstractIn addition to statistical graph properties (diameter, degree, clustering, etc.), Kleinberg [The small-world phenomenon: an algorithmic perspective, in: Proc. 32nd ACM Symp. on Theory of Computing (STOC), 2000, pp. 163-170] showed that a small-world can also be seen as a graph in which the routing task can be efficiently and easily done in spite of a lack of global knowledge. More precisely, in a lattice network augmented by extra random edges (but not chosen uniformly), a short path of polylogarithmic expected length can be found using a greedy algorithm with a local knowledge of the nodes. We call such a graph a navigable small-world since short paths exist and can be followed with partial knowledge of the network. In this paper, we show that a wide class of graphs can be augmented into navigable small-worlds.