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John Fernley

Publications and source records attributed to John Fernley.

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Percolation of the contact process on the regular tree

The contact process on the regular tree $\mathbb{T}_d$ when $d\geq 3$ has the two phase transitions of global and of local survival, found by Pemantle and Liggett at values $\lambda_1$ and $\lambda_2$. We start the system with every vertex infected and let it relax to what is known as the upper invariant infection. In this stationary state, $\lambda_p$ is the critical value beyond which the infected vertices can percolate through $\mathbb{T}_d$, and $\lambda_{p^\complement}$ is the parameter before which the healthy vertices can percolate. We find these are both distinct phase transitions \[0<\lambda_1<\lambda_p<\lambda_2<\lambda_{p^\complement}<+\infty\] on $\mathbb{T}_d$ when $d\geq 7$. The most interesting of these comparisons is $\lambda_1<\lambda_p$, which we find for all $d\geq 3$. This comparison $\lambda_1<\lambda_p$ is a long-standing open question on $\mathbb{Z}^d$ with $d\geq 2$ and was not yet found on any other graphs except where $\lambda_p$ is infinite.

math.PR

The voter model on the hyperbolic graph

We consider the voter model on the giant component of a hyperbolic random graph, which is a spatial scale-free network, in the sparse and linear-giant regime $\alpha\in(1/2,1)$. We find that the quenched expected consensus time has order $n^{2-1/\alpha}$, as the number of vertices $n\to\infty$, with probability arbitrarily close to one. This is generalised to the voter model where each vertex changes its opinion at rates $q(v)={\rm d}(v)^\varphi$, where we also establish the consensus time orders for all $\varphi\geq 0$. These orders have 3 regimes, with a phase transition at $\varphi=2-2\alpha$. For the upper bounds, our main proof idea is to connect the meeting set to some fixed target vertex of appropriate height in the product chain electrical network, to make rigorous an argument due to Durrett.

math.PR

The contact process on a bipartite spatial network

We study the contact process on a random bipartite connection hypergraph generated from two Poisson point processes, with mark-dependent connection thresholds. For asymmetric infection rates and asymmetric power law tail decays of the two degree distributions, we determine the dominant survival strategies in all parameter regimes and provide asymptotics for the epidemic probability up to logarithmic factors.

math.PR

The Supermarket Model on a Dynamic Regular Hypergraph

The supermarket model is a system of $n$ queues each with serving rates $1$ and arrival rates $\lambda$ per vertex, where tasks will move on arrival to the shortest adjacent queue. We consider the supermarket model in the small $\lambda$ regime on a large dynamic configuration hypergraph with stubs swapping their hyperedge membership at rate $\kappa$. This interpolates previous investigations of the supermarket model on static graphs of bounded degree (where an exponential tail produces a logarithmic queue) and with independently drawn neighbourhoods (where the ``power of two choices'' phenomenon is a doubly logarithmic queue). We find with high probability, over any polynomial timeframe, the order of the longest queue is \[ \log\log n + \frac{\log n}{\log \kappa} \wedge \log n \] so in the sense of controlling the order of maximal queue length, we identify which speed orders are sufficiently fast that there is no gain in moving the environment faster. Additional results describe mixing of the system and propagation of chaos over time.

math.PR

The Second Phase Transition of the Contact Process on a Random Regular Graph

The regular tree corresponds to the random regular graph as its local limit. For this reason the famous double phase transition of the contact process on regular tree has been seen to correspond to a phase transition on the large random regular graph, at least at the first critical value. In this article, we find a phase transition on that large finite graph at the second critical value: between linear reinfections and reinfections following a long healthy period.

math.PR

The Phase Transition of the Voter Model on Evolving Scale-Free Networks

The voter model is a classical interacting particle system explaining consensus formation on a social network. Real social networks feature not only a heterogeneous degree distribution but also connections changing over time. We study the voter model on a rank one scale-free network evolving in time by each vertex \emph{updating} (refreshing its edge neighbourhood) at any rate $\kappa=\kappa(N)$. We find the dynamic giant component phase transition in the consensus time of the voter model: when $\kappa\ll \tfrac{1}{N}$, the subcritical graph parameters are slower by a factor of $\tfrac{N}{\log N}$. Conversely, when $\kappa \gg 1$ the effect of the giant is removed completely and so for either graph parameter case we see consensus time on the same order as in the static supercritical case (up to polylogarithmic corrections). The intermediate dynamic speeds produce consensus time for subcritical network parameters longer not by the previous factor $\tfrac{N}{\log N}$, but by the factor $\tfrac{1}{\kappa}$.

math.PR

Simultaneous Cutoff on the Multitype Configuration Model

We find Gaussian cutoff profiles for the total variation distance to stationarity of a random walk on a multiplex network: a finite number of directed configuration models sharing a vertex set, each with its own bounded degree distribution and edge probability. Further we consider the minimal total variation distance over this space of possible doubly stochastic edge probabilities at each point in time. Looking at all possible dynamics simultaneously on one realisation of the random graph, we find that this sequence of minimal distances converges in probability to the same cutoff profile as the chain with entropy maximising transition probabilities.

math.PR

The Contact Process on a Graph Adapting to the Infection

We show existence of a non-trivial phase transition for the contact process, a simple model for infection without immunity, on a network which reacts dynamically to the infection trying to prevent an epidemic. This network initially has the distribution of an Erd\H{o}s-R\'enyi graph, but is made adaptive via updating in only the infected neighbourhoods, at constant rate. Under these graph dynamics, the presence of infection can help to prevent the spread and so many monotonicity-based techniques fail. Instead, we approximate the infection by an idealised model, the contact process on an evolving forest (CPEF). In this model updating events of a vertex in a tree correspond to the creation of a new tree with that vertex as the root. By seeing the newly created trees as offspring, the CPEF generates a Galton-Watson tree and we use that it survives if and only if its offspring number exceeds one. It turns out that the phase transition in the adaptive model occurs at a different infection rate when compared to the non-adaptive model.

math.PR

Targeted Immunisation Thresholds for the Contact Process on Power-Law Trees

Scale-free configuration models are intimately connected to power law Galton-Watson trees. It is known that contact process epidemics can propagate on these trees and therefore these networks with arbitrarily small infection rate, and this continues to be true after uniformly immunising a small positive proportion of vertices. So, we instead immunise those with largest degree: above a threshold for the maximum permitted degree, we discover the epidemic with immunisation has survival probability similar to without, by duality corresponding to comparable metastable density. With maximal degree below a threshold on the same order, this survival probability is severely reduced or zero.

math.PR

A universal right tail upper bound for supercritical Galton-Watson processes with bounded offspring

We consider a supercritical Galton-Watson process $Z_n$ whose offspring distribution has mean $m>1$ and is bounded by some $d\in \{2,3,\ldots\}$. As well-known, the associated martingale $W_n=Z_n/m^n$ converges a.s. to some nonnegative random variable $W_\infty$. We provide a universal upper bound for the right tail of $W_\infty$ and $W_n$, which is uniform in $n$ and in all offspring distributions with given $m$ and $d$, namely: \[ P(W_n\ge x)\le c_1 \exp\left\{-c_2 \frac {m-1}m \frac x d\right\}, \quad \forall n\in \mathbb N \cup \{+\infty\}, \forall x\ge 0, \] for some explicit constants $c_1,c_2>0$. For a given offspring distribution, our upper bound decays exponentially as $x\to \infty$, which is actually suboptimal, but our bound is $\textit{universal}$: it provides a single $\textit{effective}$ expression, which is $\textit{nonasymptotic}$ - it does not require $x$ large - and valid simultaneously for all supercritical bounded offspring distributions.

math.PR

Discursive Voter Models on the Supercritical Scale-Free Network

The voter model is a classical interacting particle system, modelling how global consensus is formed by local imitation. We analyse the time to consensus for a particular family of voter models when the underlying structure is a scale-free inhomogeneous random graph, in the high edge density regime where this graph features a giant component. In this regime, we verify that the polynomial orders of consensus agree with those of their mean-field approximation in [Moinet et al., 2018]. This "discursive" family of models has a symmetrised interaction to better model discussions, and is indexed by a temperature parameter which, for certain parameters of the power law tail of the network's degree distribution, is seen to produce two distinct phases of consensus speed. Our proofs rely on the well-known duality to coalescing random walks and a control on the mixing time of these walks, using the known fast mixing of the Erd\H{o}s-R\'enyi giant subgraph. Unlike in the subcritical case [Fernley and Ortgiese, 2022] which requires tail exponent of the limiting degree distribution $\tau=1+1/\gamma >3$ as well as low edge density, in the giant component case we also address the "ultrasmall world" power law exponents $\tau \in (2,3]$.

math.PR

Voter models on subcritical inhomogeneous random graphs

The voter model is a classical interacting particle system modelling how consensus is formed across a network. We analyse the time to consensus for the voter model when the underlying graph is a subcritical scale-free random graph. Moreover, we generalise the model to include a `temperature' parameter. The interplay between the temperature and the structure of the random graph leads to a very rich phase diagram, where in the different phases different parts of the underlying geometry dominate the time to consensus. Finally, we also consider a discursive voter model, where voters discuss their opinions with their neighbours. Our proofs rely on the well-known duality to coalescing random walks and a detailed understanding of the structure of the random graphs.

math.PR