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arXiv 1904.02585math.PR
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相互作用过程稀疏网络的局部weak convergence

Local weak convergence for sparse networks of interacting processes

Daniel Lacker, Kavita Ramanan, Ruoyu Wu

摘要:

我们研究了由大型稀疏图索引的相互作用粒子系统的极限行为,该系统根据离散时间马尔可夫链或扩散演化,其中粒子仅与图中最近的邻居直接相互作用。为了编码稀疏性,我们在标记(随机)图的局部弱收敛框架中工作。我们表明,联合法律的粒子系统不断变化的局部弱收敛的基础图标记的初始条件。此外,我们表明,全球经验措施收敛到一个非随机极限的一个大类的图序列,包括稀疏Erdös-Rényi图和配置模型,而经验措施的连通组件的一致随机顶点收敛到一个随机极限。一路上,我们开发了一些相关的结果的时间传播的遍历性和经验领域的收敛,以及一些一般性的结果的局部弱收敛的吉布斯措施的唯一性制度,这似乎是新的。这里得到的结果也是有用的获得自治的描述的边缘动力学的相互作用扩散和马尔可夫链稀疏图。虽然密集图上的相互作用粒子系统的限制已经被广泛研究,但一般性地研究稀疏区域的工作相对较少。

英文摘要:

We study the limiting behavior of interacting particle systems indexed by large sparse graphs, which evolve either according to a discrete time Markov chain or a diffusion, in which particles interact directly only with their nearest neighbors in the graph. To encode sparsity we work in the framework of local weak convergence of marked (random) graphs. We show that the joint law of the particle system varies continuously with respect to local weak convergence of the underlying graph marked with the initial conditions. In addition, we show that the global empirical measure converges to a non-random limit for a large class of graph sequences including sparse Erdös-Rényi graphs and configuration models, whereas the empirical measure of the connected component of a uniformly random vertex converges to a random limit. Along the way, we develop some related results on the time-propagation of ergodicity and empirical field convergence, as well as some general results on local weak convergence of Gibbs measures in the uniqueness regime which appear to be new. The results obtained here are also useful for obtaining autonomous descriptions of marginal dynamics of interacting diffusions and Markov chains on sparse graphs. While limits of interacting particle systems on dense graphs have been extensively studied, there are relatively few works that have studied the sparse regime in generality.

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