output
19972026
most citedNetworks beyond pairwise interactions: structure and dynamics

1.6k citations

Showing 2018 · math.PRShow all

11 papers · 2 filters

math.PR2018★ 2 cited

Einstein relation for random walk in a one-dimensional percolation model

Nina Gantert, Matthias Meiners, Sebastian Müller

We consider random walks on the infinite cluster of a conditional bond percolation model on the infinite ladder graph. In a companion paper, we have shown that if the random walk i…

math.PR2018★ 10 cited

Cutoff at the entropic time for random walks on covered expander graphs

Charles Bordenave, Hubert Lacoin

It is a fact simple to establish that the mixing time of the simple random walk on a d-regular graph with n vertices is asymptotically bounded from below by $d/ ((d-2)\log (d…

math.PR2018

Extremal particles of two-dimensional Coulomb gases and random polynomials on a positive background

Raphael Butez, David García-Zelada

We study the outliers for two models which have an interesting connection. On the one hand, we study a specific class of planar Coulomb gases which are determinantal. It correspond…

math.PR2018★ 12 cited

Asymptotic exponential law for the transition time to equilibrium of the metastable kinetic Ising model with vanishing magnetic field

Alexandre Gaudillière, Paolo Milanesi, Maria Eulália Vares

We consider a Glauber dynamics associated with the Ising model on a large two-dimensional box with with minus boundary conditions and in the limit of a vanishing positive external…

math.PR2018

Large Deviations of the Exit Measure through a Characteristic Boundary for a Poisson driven SDE

Etienne Pardoux, Brice Samegni-Kepgnou

Let O the basin of attraction of the unique stable equilibrium of a dynamical system, which is the law of large numbers limit of a Poissonian SDE. We consider the law of the exit p…

math.PR2018★ 125 cited

Stochastic epidemics in a homogeneous community

Tom Britton, Etienne Pardoux

These notes describe stochastic epidemics in a homogenous community. Our main concern is stochastic compartmental models (i.e. models where each individual belongs to a compartment…