Matthew Junge

From transience to recurrence with Poisson tree frogs

Consider the following interacting particle system on the d-ary tree, known as the frog model: Initially, one particle is awake at the root and i.i.d. Poisson many particles are sleeping at every other vertex. Particles that are awake perform simple random walks, awakening any sleeping particles they encounter. We prove that there is a phase transition between transience and recurrence as the initial density of particles increases, and we give the order of the transition up to a logarithmic factor.

Recurrence and transience for the frog model on trees

The frog model is a growing system of random walks where a particle is added whenever a new site is visited. A longstanding open question is how often the root is visited on the infinite dd-ary tree. We prove the model undergoes a phase transition, finding it recurrent for d=2d=2 and transient for d≥5d≥5. Simulations suggest strong recurrence for d=2d=2, weak recurrence for d=3d=3, and transience for d≥4d≥4. Additionally, we prove a 0–1 law for all dd-ary trees, and we exhibit a graph on which a 0–1 law does not hold. To prove recurrence when d=2d=2, we construct a recursive distributional equation for the number of visits to the root in a smaller process and show the unique solution must be infinity a.s. The proof of transience when d=5d=5 relies on computer calculations for the transition probabilities of a large Markov chain. We also include the proof for d≥6d≥6, which uses similar techniques but does not require computer assistance.

The critical density for the frog model is the degree of the tree

The frog model on the rooted d-ary tree changes from transient to recurrent as the number of frogs per site is increased. We prove that the location of this transition is on the same order as the degree of the tree.

Stochastic orders and the frog model

The frog model starts with one active particle at the root of a graph and some number of dormant particles at all nonroot vertices. Active particles follow independent random paths, waking all inactive particles they encounter. We prove that certain frog model statistics are monotone in the initial configuration for two nonstandard stochastic dominance relations: the increasing concave and the probability generating function orders. This extends many canonical theorems. We connect recurrence for random initial configurations to recurrence for deterministic configurations. Also, the limiting shape of activated sites on the integer lattice respects both of these orders. Other implications include monotonicity results on transience of the frog model where the number of frogs per vertex decays away from the origin, on survival of the frog model with death, and on the time to visit a given vertex in any frog model.

Site recurrence for coalescing random walk

Begin continuous time random walks from every vertex of a graph and have particles coalesce when they collide. We use a duality relation with the voter model to prove the process is site recurrent on bounded degree graphs, and for Galton-Watson trees whose offspring distribution has exponential tail. We prove bounds on the occupation probability of a site, as well as a general 0-1 law. Similar conclusions hold for a coalescing process on trees where particles do not backtrack.

Asymptotic behavior of the Brownian frog model

We introduce an extension of the frog model to Euclidean space and prove properties for the spread of active particles. The new geometry introduces a phase transition that does not occur for the frog model on the lattice. Fix

Block size in Geometric(

r>0

p

and place a particle at each point

)-biased permutations

x

The upper threshold in ballistic annihilation

of a unit intensity Poisson point process