The combination of the compactness of networks, featuring small diameters, and their
complex architectures results in a variety of critical effects dramatically different from those in …

Motivated by the concept of geometrical frustration, we introduce a class of statistical
mechanics lattice models for the glass transition. Monte Carlo simulations in three …

Many problems of interest in computer science and information theory can be phrased in
terms of a probability distribution over discrete variables associated to the vertices of a large …

Randomized search algorithms for hard combinatorial problems exhibit a large variability of
performances. We study the different types of rare events which occur in such out-of …

Efficient sampling from a classical Gibbs distribution is an important computational problem
with applications ranging from statistical physics over Monte Carlo and optimization …

We consider “lattice glass models” in which each site can be occupied by at most one
particle, and any particle may have at most ℓ occupied nearest neighbors. Using the cavity …

We study both numerically and analytically what happens to a random graph of average
connectivity α when its leaves and their neighbors are removed iteratively up to the point …

Drawing independent samples from a probability distribution is an important computational
problem with applications in Monte Carlo algorithms, machine learning, and statistical …

Computer science and physics have been closely linked since the birth of modern
computing. In recent years, an interdisciplinary area has blossomed at the junction of these …

Recently, as the controllability of complex networks attracts much attention, how to design
and optimize the controllability of networks has become a common and urgent problem in …