Planar reinforced $k$-out percolation

TL;DR

This paper studies the percolation behavior of a planar reinforced network model where edges are incrementally reinforced based on a probabilistic rule, providing theoretical results and numerical verification for different reinforcement intensities.

Contribution

It introduces a new planar reinforced percolation model with a probabilistic reinforcement rule and establishes percolation conditions for both infinite and finite reinforcement parameters.

Findings

Percolation occurs for $k=2$ with infinite reinforcement after sparse sprinkling.

Finite-size criteria for percolation are derived and verified numerically.

Percolation behavior depends on the reinforcement parameter $eta$, with results extending to finite $eta$.

Abstract

We investigate the percolation properties of a planar reinforced network model. In this model, at every time step, every vertex chooses $k \ge 1$ incident edges, whose weight is then increased by 1. The choice of this $k$-tuple occurs proportionally to the product of the corresponding edge weights raised to some power $\alpha > 0$. Our investigations are guided by the conjecture that the set of infinitely reinforced edges percolates for $k = 2$ and $\alpha \gg 1$. First, we study the case $\alpha = \infty$, where we show the percolation for $k = 2$ after adding arbitrarily sparse independent sprinkling and also allowing dual connectivities. We also derive a finite-size criterion for percolation without sprinkling. Then, we extend this finite-size criterion to the $\alpha < \infty$ case. Finally, we verify these conditions numerically.