Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA limit theorem for diffusions on graphs with variable configuration
79
0
0.0
(
0
)
Authors
Alexey M. Kulik
Ask ChatGPT about the research
No Arabic abstract
A limit theorem for a sequence of diffusion processes on graphs is proved in a case when vary both parameters of the processes (the drift and diffusion coefficients on every edge and the asymmetry coefficients in every vertex), and configuration of graphs, where the processes are set on. The explicit formulae for the parameters of asymmetry for the vertices of the limiting graph are given in the case, when, in the pre-limiting graphs, some groups of vertices form knots contracting into a points.
rate research
Read More
We study a stochastic compartmental susceptible-infected (SI) epidemic process on a configuration model random graph with a given degree distribution over a finite time interval $[0,T],$ for some $ T>0$. In this setting, we split the population of graph nodes into two compartments, namely, $S$ and $I$, denoting the susceptible and infected nodes, respectively. In addition to the sizes of these two compartments, we study counts of $SI$-edges (those connecting a susceptible and an infected node) and $SS$-edges (those connecting two susceptible nodes). We describe the dynamical process in terms of these counts and present a functional central limit theorem (FCLT) for them, a scaling limit of the dynamical process as $n$, the number of nodes in the random graph, grows to infinity. To be precise, we show that these counts, when appropriately scaled, converge weakly to a continuous Gaussian vector martingale process the usual Skorohod space of real 3-dimensional vector-valued cadlag, functions on $[0,T]$ endowed with the Skorohod topology. We assume certain technical requirements for this purpose. We discuss applications of our FCLT in percolation theory (from a non-equilibrium statistical mechanics point of view), and in computer science in the context of spread of computer viruses. We also provide simulation results for some common degree distributions.
For a difference approximations of multidimensional diffusion, the truncated local limit theorem is proved. Under very mild conditions on the distribution of the difference terms, this theorem provides that the transition probabilities of these approximations, after truncation of some asymptotically negligible terms, possess a densities that converge uniformly to the transition probability density for the limiting diffusion and satisfy a uniform diffusion-type estimates. The proof is based on the new version of the Malliavin calculus for the product of finite family of measures, that may contain non-trivial singular components. An applications for uniform estimates for mixing and convergence rates for difference approximations to SDEs and for convergence of difference approximations for local times of multidimensional diffusions are given.
We consider bootstrap percolation and diffusion in sparse random graphs with fixed degrees, constructed by configuration model. Every node has two states: it is either active or inactive. We assume that to each node is assigned a nonnegative (integer) threshold. The diffusion process is initiated by a subset of nodes with threshold zero which consists of initially activated nodes, whereas every other node is inactive. Subsequently, in each round, if an inactive node with threshold $theta$ has at least $theta$ of its neighbours activated, then it also becomes active and remains so forever. This is repeated until no more nodes become activated. The main result of this paper provides a central limit theorem for the final size of activated nodes. Namely, under suitable assumptions on the degree and threshold distributions, we show that the final size of activated nodes has asymptotically Gaussian fluctuations.
Any (measurable) function $K$ from $mathbb{R}^n$ to $mathbb{R}$ defines an operator $mathbf{K}$ acting on random variables $X$ by $mathbf{K}(X)=K(X_1, ldots, X_n)$, where the $X_j$ are independent copies of $X$. The main result of this paper concerns selectors $H$, continuous functions defined in $mathbb{R}^n$ and such that $H(x_1, x_2, ldots, x_n) in {x_1,x_2, ldots, x_n}$. For each such selector $H$ (except for projections onto a single coordinate) there is a unique point $omega_H$ in the interval $(0,1)$ so that for any random variable $X$ the iterates $mathbf{H}^{(N)}$ acting on $X$ converge in distribution as $N to infty$ to the $omega_H$-quantile of $X$.
Let $mathcal{G} = {G_1 = (V, E_1), dots, G_m = (V, E_m)}$ be a collection of $m$ graphs defined on a common set of vertices $V$ but with different edge sets $E_1, dots, E_m$. Informally, a function $f :V rightarrow mathbb{R}$ is smooth with respect to $G_k = (V,E_k)$ if $f(u) sim f(v)$ whenever $(u, v) in E_k$. We study the problem of understanding whether there exists a nonconstant function that is smooth with respect to all graphs in $mathcal{G}$, simultaneously, and how to find it if it exists.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
