Four universal growth regimes in degree-dependent first passage percolation on spatial random graphs I

TL;DR

This paper identifies four universal growth regimes in degree-dependent first passage percolation on spatial random graphs, revealing how transmission times scale with distance under various model parameters.

Contribution

It introduces a comprehensive analysis of phase transitions in spatial random graphs with degree-dependent transmission times, including the novel polynomial growth phase.

Findings

Four universal growth regimes identified: explosive, polylogarithmic, polynomial, and linear.

The polynomial growth phase is a new phenomenon in spatial graph models.

Transition points depend on degree distribution tail, long-range parameters, and edge transmission behavior.

Abstract

One-dependent first passage percolation is a spreading process on a graph where the transmission time through each edge depends on the direct surroundings of the edge. In particular, the classical iid transmission time $L_{xy}$ is multiplied by $(W_xW_y)^\mu$, a polynomial of the expected degrees $W_x, W_y$ of the endpoints of the edge $xy$, which we call the penalty function. Beyond the Markov case, we also allow any distribution for $L_{xy}$ with regularly varying distribution near $0$. We then run this process on three spatial scale-free random graph models: 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 show that as the penalty-function, i.e., $\mu$ increases, the transmission time between two far away vertices sweeps through four universal phases: explosive (with tight transmission times), polylogarithmic, polynomial but strictly sublinear, and linear in the Euclidean distance. The strictly polynomial growth phase here is a new phenomenon that so far was extremely rare in spatial graph models. The four growth phases are highly robust in the model parameters and are not restricted to phase boundaries. Further, the transition points between the phases depend non-trivially on the main model parameters: the tail of the degree distribution, a long-range parameter governing the presence of long edges, and the behaviour of the distribution $L$ near $0$. In this paper we develop new methods to prove the upper bounds in all sub-explosive phases. Our companion paper complements these results by providing matching lower bounds in the polynomial and linear regimes.