Joost Jorritsma
joint with Júlia Komjáthy, Dieter Mitsche
Junior Probability Seminar University of Oxford, November '24
Hyperbolic random graph
Scale-free percolation
Long-range percolation
Scale-free Gilbert RG
Random geom. graph
Nearest-neighbor percolation
Vertex set \(\mathcal{V}_\infty\)
Edge set \(\mathcal{E}_\infty\)
Connection probability
$$\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\bigg(\beta\frac{\kappa(w_u, w_v)}{\|x_u-x_v\|^d}\bigg)^\alpha\wedge 1$$
Vertex set \(\mathcal{V}_\infty\)
$$\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\bigg(\beta\frac{\kappa(w_u, w_v)}{\|x_u-x_v\|^d}\bigg)^\alpha$$
$$\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\phantom{\bigg(\beta}\frac{\kappa(w_u, w_v)}{\phantom{\|x_u-x_v\|^d}}\phantom{\bigg)^\alpha\wedge 1}$$
$$\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\phantom{\bigg(\beta}\frac{\kappa(w_u, w_v)}{\|x_u-x_v\|^d}\phantom{\bigg)^\alpha\wedge 1}$$
$$\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\bigg(\phantom{\beta}\frac{\kappa(w_u, w_v)}{\|x_u-x_v\|^d}\bigg)^\alpha\phantom{\wedge 1}$$
Edge set \(\mathcal{E}_\infty\)
Connection probability
$$\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\bigg(\beta\frac{\kappa(w_u, w_v)}{\|x_u-x_v\|^d}\bigg)^\alpha\wedge 1$$
Vertex set \(\mathcal{V}_\infty\)
Geom. Inhom. RG
Hyperbolic RG
Geom. RG
Long-range perc.
Scale-free Gilbert RG
Age-dependent RCM
Theorem. When \(\tau<2\) or \(\alpha<1\):
[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 \(\tau>2\) and \(\alpha>1\):
$$\mathbb{P}(\mathrm{deg}(0)\ge k)\sim k^{-(\tau-1)}.$$
\(\tau\) small: many hubs
\(\alpha\) small: many long edges
Connection probability
$${\color{grey}\mathbb{P}\big(u\leftrightarrow v\mid \mathcal{V}_\infty\big)=\bigg(\beta}\frac{\kappa(w_u, w_v)}{\color{grey}\|x_u-x_v\|^d}{\color{grey}\bigg)^\alpha\wedge 1}$$
A parameterized kernel: \(\sigma\ge 0\)
$$\kappa_{\sigma}(w_u, w_v):=\max\{w_u, w_v\}\min\{w_u, w_v\}^\sigma$$
SFP/GIRG
Hyperbolic RG
Age-dep. RCM
Scale-free Gilbert
Long-range percolation
Random geom. graph
Nearest-neighbor percolation
Questions
Questions
[Alexander & Chayes & Chayes '90], [Grimmett & Marstrand, '90], [Kesten & Zhang '90], ..., [Deuschel, Pisztora, '96], [Biskup '04], [Penrose '05], [Sly & Crawford'12], [Kiwi & Mitsche '17], [Lichev, Lodewijks, Mitsche, Schapira '22], [Bläsius, Friedrich, Ruff, Zeiff, '23]
Lower tail large deviations of the giant*:
*log(log)-corrections at phase transition
Small components*:
If \(\mathbb{E}[\# \text{edges of length } \Omega(n^{1/d})]\to\infty\)
If \(\zeta>(d-1)/d\)
Aim: Find minimal \(\zeta\) s.t.
Aim: Find \(\gamma\) s.t.
Aim: Find minimal \(\zeta\) s.t.
Aim: Find \(\gamma\) s.t.
(FKG)
\(\delta\) small
# possibilities for \(|{\color{red}2^{\mathrm{nd}}\text{-largest}}|\ge k\)
\(\kappa_\mathrm{prod}=w_u w_v\)
\(\kappa_\mathrm{max}=w_u\vee w_v\)
Description of weight distribution in large components
Prevent "small-to-large" merging
Open problems:
Answered questions (\(d=1\))
[Erdős, Rényi '59; O'Connell '98; Andreis, König, Langhammer, Patterson'23]
Exponential growth of neighbourhood
The largest component
[Alexander, Chayes, Chayes '90; Cerf '00; Gandolfi '88; Grimmett & Marstrand '90, Kesten & Zhang '90, Pisztora '96]
Surface-tension driven behavior
The largest component
Remarks.
No known results
Example 1:
Scale-free Gilbert RG in \(d=1\)
Power-law degrees: \(\tau>2\)
\(d\ge 2\)
* [Gracar, Lüchtrath, Mönch '22]
Theorem. (J., Komjáthy, Mitsche '23+)
Set
If \(\zeta_\ast>0\), then LLN for \(|{\color{blue}\text{largest}}|\), and
Long-range parameter: \(\alpha>1\)
\(\kappa_\mathrm{max}=w_u\vee w_v\)
\(\kappa_\mathrm{prod}=w_uw_v\)
Example 2:
Geom. Inhomog. RG in \(d=1\)
# possibilities for \(|{\color{red}2^{\mathrm{nd}}\text{-largest}}|\ge k\)
Questions
Questions
[Alexander & Chayes & Chayes '90], [Grimmett & Marstrand, '90], [Kesten & Zhang '90], ..., [Deuschel, Pisztora, '96], [Biskup '04], [Penrose '05], [Sly & Crawford'12], [Kiwi & Mitsche '17], [Lichev, Lodewijks, Mitsche, Schapira '22], [Bläsius, Friedrich, Ruff, Zeiff, '23]
Description of weight distribution in large components
Prevent "small-to-large" merging
Answered questions (\(d=1\))
Open problems:
Answered questions (\(d=1\))
Small
Large
Remarks.
Power-law degrees: \(\tau>2\)
\(d\ge 2\)
* [Gracar, Lüchtrath, Mönch '22]
Theorem. (J., Komjáthy, Mitsche '23+)
Set
If \(\zeta_\ast>0\), then LLN for \(|{\color{blue}\text{largest}}|\), and
Long-range parameter: \(\alpha>1\)
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