Finite Percolation at a Multiple of the Threshold

Abstract

Bond percolation on infinite heavy-tailed power-law random networks lacks a proper phase transition; or one may say, there is a phase transition at {\em zero percolation probability}. Nevertheless, a finite size percolation threshold $q_c(N)$, where $N$ is the network size, can be defined. For such heavy-tailed networks, one can choose a percolation probability $q(N)=ρq_c(N)$ such that $\displaystyle \lim_{N\to \infty}(q-q_c(N)) =0$, and yet $ρ$ is arbitrarily large (such a scenario does not exist for networks with non-zero percolation threshold). We find that the critical behavior of random power-law networks is best described in terms of $ρ$ as the order parameter, rather than $q$. This paper makes the notion of the phase transition of the size of the largest connected component at $ρ=1$ precise. In particular, using a generating function based approach, we show that for $ρ>1$, and the power-law exponent, $2\leq τ<3$, the largest connected component scales as $\sim N^{1-1/τ}$, while for $0<ρ<1$ the scaling is at most $\sim N^{\frac{2-τ}τ}$; here, the maximum degree of any node, $k_{max}$, has been assumed to scale as N^{1/τ}$. In general, our approach yields that for large $N$, $ρ\gg 1$, $2\leq τ<3$, and $k_{max} \sim N^{1/τ}$, the largest connected component scales as $\sim ρ^{1/(3-τ)}N^{1-1/τ}$.Thus, for any fixed but large N, we recover, and make it precise, a recent result that computed a scaling behavior of $q^{1/(3-τ)}$ for "small $q$". We also provide large-scale simulation results validating some of these scaling predictions, and discuss applications of these scaling results to supporting efficient unstructured queries in peer-to-peer networks.