Polynomial growth in degree-dependent first passage percolation on spatial random graphs

TL;DR

This paper analyzes how polynomial penalties on transmission times in degree-dependent first passage percolation affect the spreading phases on spatial random graphs with power-law degrees, revealing four universal phases.

Contribution

It introduces a novel non-Markovian percolation model with degree-dependent transmission times and characterizes phase transitions on spatial scale-free graphs.

Findings

Four universal phases of transmission times identified

Explicit thresholds for phase transitions derived

Proofs of bounds for different phases provided

Abstract

In this paper we study a version of (non-Markovian) first passage percolation on graphs, where the transmission time between two connected vertices is non-iid, but increases by a penalty factor polynomial in their expected degrees. Based on the exponent of the penalty-polynomial, this makes it increasingly harder to transmit to and from high-degree vertices. This choice is motivated by awareness or time-limitations. For the iid part of the transmission times we allow any nonnegative distribution with regularly varying behaviour at $0$. For the underlying graph models we choose spatial random graphs that have power-law degree distributions, so that the effect of the penalisation becomes visible: (finite and infinite) Geometric Inhomogeneous Random Graphs, and Scale-Free Percolation. In these spatial models, the connection probability between two vertices depends on their spatial distance and on their expected degrees. We prove that upon increasing the penalty exponent, the transmission time between two far away vertices $x,y$ sweeps through four universal phases even for a single underlying graph: explosive (tight transmission times), polylogarithmic, polynomial but sublinear ($|x-y|^{\eta_0+o(1)}$ for an explicit $\eta_0<1$), and linear ($\Theta(|x-y|)$) in their Euclidean distance. Further, none of these phases are restricted to phase boundaries, and those are non-trivial in the main model parameters: the tail of the degree-distribution, a long-range parameter, and the exponent of regular variation of the iid part of the transmission times. In this paper we present proofs of lower bounds for the latter two phases and the upper bound for the linear phase. These complement the matching upper bounds for the polynomial regime in our companion paper.