Joost Jorritsma
joint with Júlia Komjáthy, Dieter Mitsche
Long-range phenomena in percolation, September '24
[Gandolfi '88, Grimmett & Marstrand '90, Kesten & Zhang '90, Pisztora '96]
Surface-tension driven behavior
The largest component
Edge set E∞
Connection probability
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
Vertex set V∞
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
(deg(v)∣wv=w)=Poi(cα,d,τpβ⋅w)
Supercriticality assumption
Edge set E∞
Vertex set V∞
Questions:
Fixed parameters:
[Gracar, Lüchtrath, Mönch '21]
or d=1, and α∈(1,2∨ατ)
Power-law weights
Constant weights
Lower tail*:
If E[#edges of length n1/d]→∞
*log-corrections at phase transition
Upper tail:
If non-critical
*at continuity points of I(θ+ε)
Biskup ['04]
Goal 1:
+ local convergence (Giant is almost local)
[vdHofstad, vdHoorn, Maitra '21, vd Hofstad '21]
Goal 2: Prove the theorem
Bootstrapping: Goal 1 proved via method Goal 2
Edge set E∞
Connection probability
Vertex set V∞
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
Power-law weights
Constant weights
Lower tail*:
If E[#edges of length n1/d]→∞
*log-corrections at phase transition
Upper tail:
If non-critical
*at continuity points of I(θ+ε)
Related work:
P(u↔v∣V∞)=p(∥xu−xv∥dwu1/d+wv1/d∧1)dα
Challenge: Capture dependency on percolation p
Geom. inhom. RG
Long-range percolation
Lower tail*:
If E[#edges of length n1/d]→∞
*log-corrections at phase transition
Upper tail:
If non-critical, ρ∈(θ,1)
*at continuity points of I(ρ)
Vertex set V∞
Edge set E∞
Connection probability
P(u↔v∣V∞)=(β∥xu−xv∥dκ(wu,wv))α∧1
Vertex set V∞
Edge set E∞
Geom. Inhom. RG
Hyperbolic RG
Geom. RG
Long-range perc.
Scale-free Gilbert RG
Age-dependent RCM
Theorem. When τ<2 or α<1:
[Deijfen, v.d. Hofstad, Hooghiemstra '13], [Gracar, Grauer, Lüchtrath, Mörters '18]
[Heydenreich, Hulshof, J. '17], [Hirsch '17], [v.d. Hofstad, v.d. Hoorn, Maitra '22],
[J., Komjáthy, Mitsche, '23], [Lüchtrath '22]
Theorem. When τ>2 and α>1:
P(deg(0)≥k)∼k−(τ−1).
τ small: many hubs
α small: many long edges
Connection probability
P(u↔v∣V∞)=(β∥xu−xv∥dκ(wu,wv))α∧1
A parameterized kernel: σ≥0
κσ(wu,wv):=max{wu,wv}min{wu,wv}σ
SFP/GIRG
Hyperbolic RG
Age-dep. RCM
Scale-free Gilbert
Long-range percolation
Random geom. graph
Nearest-neighbor percolation
Remarks.
No known results
Example 1:
Scale-free Gilbert RG in d=1
Power-law degrees: τ>2
d≥2
* [Gracar, Lüchtrath, Mönch '22]
Theorem. (J., Komjáthy, Mitsche '23+)
Set
If ζ∗>0, then LLN for ∣largest∣, and
Long-range parameter: α>1
κmax=wu∨wv
κprod=wuwv
Example 2:
Geom. Inhomog. RG in d=1
Aim: Find minimal ζ s.t.
Aim: Find γ s.t.
Aim: Find minimal ζ s.t.
Aim: Find γ s.t.
(FKG)
δ small
Vertex set V∞
Soft Poisson Boolean model
Long-range percolation
Bond percolation on Zd
Lower tail:
- surface tension
- vertex boundary
Vertex set V∞
Edge set E∞
Connection probability
P(u↔v∣V∞)=p(∥xu−xv∥dβ∧1)α
P(u↔v∣V∞)=p(∥xu−xv∥dβ∧1)α
P(u↔v∣V∞)=p(∥xu−xv∥dβ∧1)α
P(u↔v∣V∞)=p(∥xu−xv∥dβ∧1)α
P(u↔v∣V∞)=p(∥xu−xv∥d1)α∧1
P(u↔v∣V∞)=p(∥xu−xv∥d1∧1)α
Edge set E∞
Connection probability
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
Vertex set V∞
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
P(u↔v∣V∞)=p(∥xu−xv∥wu1/d+wv1/d∧1)αd
Edge set E∞
Connection probability
Vertex set V∞
P(u↔v∣V∞)=p(∥xu−xv∥dβ⋅(wu⋅wv)∧1)α
Geom. inhom. RG
Long-range percolation
Lower tail*:
If E[#edges of length n1/d]→∞
*log-corrections at phase transition
Upper tail:
If non-critical, ρ∈(θ,1)
*at continuity points of I(ρ)
Open problems:
Answered questions (d=1)
Small
Large
Remarks.
Power-law degrees: τ>2
d≥2
* [Gracar, Lüchtrath, Mönch '22]
Theorem. (J., Komjáthy, Mitsche '23+)
Set
If ζ∗>0, then LLN for ∣largest∣, and
Long-range parameter: α>1
Example 2:
Geom. Inhomog. RG in d=1
Hyperbolic random graph
Scale-free percolation
Long-range percolation
Scale-free Gilbert RG
Random geom. graph
Nearest-neighbor percolation