Phase Transitions and Percolation at Criticality in Enhanced Random Connection Models

Abstract

We study phase transition and percolation at criticality for three random graph models on the plane, viz., the homogeneous and inhomogeneous enhanced random connection models (RCM) and the Poisson stick model. These models are built on a homogeneous Poisson point process P λ in R 2 of intensity λ. In the homogeneous RCM, the vertices at x, y are connected with probability g(|x − y|), independent of everything else, where g : [0, ∞) → [0, 1] and |•| is the Euclidean norm. In the inhomogeneous version of the model, points of P λ are endowed with weights that are non-negative independent random variables with distribution P (W > w) = w −β 1 [1,∞) (w), β > 0. Vertices located at x, y with weights W x , W y are connected with probability 1 − exp − ηW x W y |x−y| α , η, α > 0, independent of all else. The graphs are enhanced by considering the edges of the graph as straight line segments starting and ending at points of P λ . A path in the graph is a continuous curve that is a subset of the union of all these line segments. The Poisson stick model consists of line segments of independent random lengths and orientation with the mid point of each segment located at a distinct point of P λ . Intersecting lines form a path in the graph. A graph is said to percolate if there is an infinite connected component or path. We derive conditions for the existence of a phase transition and show that there is no percolation at criticality.

Phase transitions and noise sensitivity on the Poisson space via stopping sets and decision trees

“…The above theorem has reduced the proof of noise sensitivity, exceptional times and sharp phase transition for crossing events at criticality in the k-percolation model to showing nondegeneracy of the crossing events and bounds for one-arm probabilities or nonpercolation at criticality. Showing these properties is a seperate percolation theoretic question, often model-specific and in the planar case, these have been achieved in some models via RSW-type estimates (see [4,7,41,45,66,67,74]). See Corollary 8.5 below for a case in which the above estimates are known.…”