Maksim Zhukovskii Moscow Institute of Physics and ......Maksim Zhukovskii Moscow Institute of...
Transcript of Maksim Zhukovskii Moscow Institute of Physics and ......Maksim Zhukovskii Moscow Institute of...
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Weak saturation in sparse random graphs
Maksim ZhukovskiiMoscow Institute of Physics and Technology
joint work with Mohamad Reza Bidgoli
Combinatorics and geometry days IIApril 13, 2020
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Percolation in graphs and weak saturation
H — a spanning subgraph of G
I F -bootstrap percolation percolation:
H = H0 ⊂ H1 ⊂ . . . ⊂ Hm = G such that Ht \Ht−1 containsexactly one edge et and et creates a new copy of F
we say that H F -percolates in G
I H is weakly F -saturated in G if
it does not contain any copy of F and it F -percolates in G
w-sat(G,F ) is the smallest number of edges in a weaklyF -saturated graph in G
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Percolation in graphs and weak saturation
H — a spanning subgraph of G
I F -bootstrap percolation percolation:
H = H0 ⊂ H1 ⊂ . . . ⊂ Hm = G such that Ht \Ht−1 containsexactly one edge et and et creates a new copy of F
we say that H F -percolates in G
I H is weakly F -saturated in G if
it does not contain any copy of F and it F -percolates in G
w-sat(G,F ) is the smallest number of edges in a weaklyF -saturated graph in G
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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K3-bootstrap percolation process
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Weak saturation in complete graphs: triangle patterns
I w-sat(Kn,K3) ≤ n− 1 since K1,n−1 is weakly K3-saturated in Kn
I w-sat(Kn,K3) ≥ n− 1 since a disconnected graph is not weaklyK3-saturated in Kn
w-sat(Kn,K3) = n− 1
What about w-sat(Kn,Ks)?
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Weak saturation in complete graphs: triangle patterns
I w-sat(Kn,K3) ≤ n− 1 since K1,n−1 is weakly K3-saturated in Kn
I w-sat(Kn,K3) ≥ n− 1 since a disconnected graph is not weaklyK3-saturated in Kn
w-sat(Kn,K3) = n− 1
What about w-sat(Kn,Ks)?
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Weak saturation in complete graphs: triangle patterns
I w-sat(Kn,K3) ≤ n− 1 since K1,n−1 is weakly K3-saturated in Kn
I w-sat(Kn,K3) ≥ n− 1 since a disconnected graph is not weaklyK3-saturated in Kn
w-sat(Kn,K3) = n− 1
What about w-sat(Kn,Ks)?
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Weak saturation in complete graphs: triangle patterns
I w-sat(Kn,K3) ≤ n− 1 since K1,n−1 is weakly K3-saturated in Kn
I w-sat(Kn,K3) ≥ n− 1 since a disconnected graph is not weaklyK3-saturated in Kn
w-sat(Kn,K3) = n− 1
What about w-sat(Kn,Ks)?
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Weak saturation in complete graphs: clique patterns
I In 1968, Bollobas conjectured thatw-sat(Kn,Ks) = (s− 2)n−
(s−12
).
I Proved by Frankl in 1982, Kalai in 1984 and Lovasz in 1977.
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Weak saturation in complete graphs: clique patterns
I In 1968, Bollobas conjectured thatw-sat(Kn,Ks) = (s− 2)n−
(s−12
).
I Proved by Frankl in 1982, Kalai in 1984 and Lovasz in 1977.
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Stability of w-sat
Theorem (Korandi, Sudakov, 2017)
Let 0 < p < 1 be a constant, s ≥ 3. Thenw-sat(G(n, p),Ks) = w-sat(Kn,Ks) with high probability.
Problem: determine the exact probability range where the weaksaturation number is (s− 2)n−
(s−12
).
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Stability of w-sat
Theorem (Korandi, Sudakov, 2017)
Let 0 < p < 1 be a constant, s ≥ 3. Thenw-sat(G(n, p),Ks) = w-sat(Kn,Ks) with high probability.
Problem: determine the exact probability range where the weaksaturation number is (s− 2)n−
(s−12
).
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Weak saturation in sparse random graphs
Theorem (M.R. Bidgoli, MZ, 2019+)
There exists p0 s.t.
I if p� p0, then w-sat(G(n, p),Ks) = w-sat(Kn,Ks) w.h.p
I if p� p0, then w-sat(G(n, p),Ks) 6= w-sat(Kn,Ks) w.h.p.
If p ≥ n− 12s−3 ln2 n,then w-sat(G(n, p),Ks) = w-sat(Kn,Ks) w.h.p.
If p ≤ cn− 2s+1 (lnn)
2(s−2)(s+1) ,
then w-sat(G(n, p),Ks) 6= w-sat(Kn,Ks) w.h.p.
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Weak saturation in sparse random graphs
Theorem (M.R. Bidgoli, MZ, 2019+)
There exists p0 s.t.
I if p� p0, then w-sat(G(n, p),Ks) = w-sat(Kn,Ks) w.h.p
I if p� p0, then w-sat(G(n, p),Ks) 6= w-sat(Kn,Ks) w.h.p.
If p ≥ n− 12s−3 ln2 n,then w-sat(G(n, p),Ks) = w-sat(Kn,Ks) w.h.p.
If p ≤ cn− 2s+1 (lnn)
2(s−2)(s+1) ,
then w-sat(G(n, p),Ks) 6= w-sat(Kn,Ks) w.h.p.
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The proof of the lower bound for K3
1. If p� 1n , then w.h.p. there are no triangles.
2. If 1n2 � p ≤
√1n , then w.h.p. the number of triangles is less then a
half of the number of edges.
3. If√
1n < p <
√lnnn , then
• w.h.p. there exists an edge that does not belong to any triangle,• w.h.p. G(n, p) is 2-connected.
I A connected spanning subgraph of Kn is weakly K3-saturated inKn.
I If a tree H is weakly K3-saturated in G(n, p), then it might containan edge e that does not belong to a triangle.
Then H \ e is weakly K3-saturated in G(n, p) \ e which, in turn, isweakly K3-saturated in G(n, p). A contradiction since H has lessthen n− 1 edges.
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The proof of the lower bound for K3
1. If p� 1n , then w.h.p. there are no triangles.
2. If 1n2 � p ≤
√1n , then w.h.p. the number of triangles is less then a
half of the number of edges.
3. If√
1n < p <
√lnnn , then
• w.h.p. there exists an edge that does not belong to any triangle,• w.h.p. G(n, p) is 2-connected.
I A connected spanning subgraph of Kn is weakly K3-saturated inKn.
I If a tree H is weakly K3-saturated in G(n, p), then it might containan edge e that does not belong to a triangle.
Then H \ e is weakly K3-saturated in G(n, p) \ e which, in turn, isweakly K3-saturated in G(n, p). A contradiction since H has lessthen n− 1 edges.
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The proof of the lower bound for K3
1. If p� 1n , then w.h.p. there are no triangles.
2. If 1n2 � p ≤
√1n , then w.h.p. the number of triangles is less then a
half of the number of edges.
3. If√
1n < p <
√lnnn , then
• w.h.p. there exists an edge that does not belong to any triangle,• w.h.p. G(n, p) is 2-connected.
I A connected spanning subgraph of Kn is weakly K3-saturated inKn.
I If a tree H is weakly K3-saturated in G(n, p), then it might containan edge e that does not belong to a triangle.
Then H \ e is weakly K3-saturated in G(n, p) \ e which, in turn, isweakly K3-saturated in G(n, p). A contradiction since H has lessthen n− 1 edges.
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The proof of the lower bound for K3
1. If p� 1n , then w.h.p. there are no triangles.
2. If 1n2 � p ≤
√1n , then w.h.p. the number of triangles is less then a
half of the number of edges.
3. If√
1n < p <
√lnnn , then
• w.h.p. there exists an edge that does not belong to any triangle,• w.h.p. G(n, p) is 2-connected.
I A connected spanning subgraph of Kn is weakly K3-saturated inKn.
I If a tree H is weakly K3-saturated in G(n, p), then it might containan edge e that does not belong to a triangle.
Then H \ e is weakly K3-saturated in G(n, p) \ e which, in turn, isweakly K3-saturated in G(n, p). A contradiction since H has lessthen n− 1 edges.
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The proof of the lower bound for K3
1. If p� 1n , then w.h.p. there are no triangles.
2. If 1n2 � p ≤
√1n , then w.h.p. the number of triangles is less then a
half of the number of edges.
3. If√
1n < p <
√lnnn , then
• w.h.p. there exists an edge that does not belong to any triangle,• w.h.p. G(n, p) is 2-connected.
I A connected spanning subgraph of Kn is weakly K3-saturated inKn.
I If a tree H is weakly K3-saturated in G(n, p), then it might containan edge e that does not belong to a triangle.
Then H \ e is weakly K3-saturated in G(n, p) \ e which, in turn, isweakly K3-saturated in G(n, p). A contradiction since H has lessthen n− 1 edges.
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The proof of the lower bound for K3
1. If p� 1n , then w.h.p. there are no triangles.
2. If 1n2 � p ≤
√1n , then w.h.p. the number of triangles is less then a
half of the number of edges.
3. If√
1n < p <
√lnnn , then
• w.h.p. there exists an edge that does not belong to any triangle,• w.h.p. G(n, p) is 2-connected.
I A connected spanning subgraph of Kn is weakly K3-saturated inKn.
I If a tree H is weakly K3-saturated in G(n, p), then it might containan edge e that does not belong to a triangle.
Then H \ e is weakly K3-saturated in G(n, p) \ e which, in turn, isweakly K3-saturated in G(n, p). A contradiction since H has lessthen n− 1 edges.
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The proof of the upper bound for K3
Let p > 2 3
√lnnn .
I w.h.p. any three vertices of G(n, p) have a common neighbor,
I w.h.p. any pair of vertices has a connected common neighborhood.
If G has the above properties, then it has a weakly K3-saturatedsubtree.
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The proof of the upper bound for K3
Let p > 2 3
√lnnn .
I w.h.p. any three vertices of G(n, p) have a common neighbor,
I w.h.p. any pair of vertices has a connected common neighborhood.
If G has the above properties, then it has a weakly K3-saturatedsubtree.
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Percolation in random graph
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Existence of the threshold
1. If p ≤ 1n lnn , then whp there are no Ks and the number of edges is
smaller than n2 .
2. If 1n lnn < p� n− 2
s+1 , then a.a.s. the number of Ks is smaller that12
(n2
)p.
3. Consider the property Bs that every pair of vertices have s− 2adjacent common neighbors.This property is increasing with sharp threshold probability
q(n) = (2(s− 2)!)2
(s+1)(s−2)n− 2s+1 (lnn)
2(s−2)(s+1) .
Consider the property As of having w-sat equal to w-sat(Gn,Ks).The property As ∩Bs is increasing with threshold r(n) ≥ q(n).
4. It remains to prove that, for n− 2s+1 ≤ p ≤ (1 + ε)q(n) the property
As does not hold a.a.s.
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Existence of the threshold
1. If p ≤ 1n lnn , then whp there are no Ks and the number of edges is
smaller than n2 .
2. If 1n lnn < p� n− 2
s+1 , then a.a.s. the number of Ks is smaller that12
(n2
)p.
3. Consider the property Bs that every pair of vertices have s− 2adjacent common neighbors.This property is increasing with sharp threshold probability
q(n) = (2(s− 2)!)2
(s+1)(s−2)n− 2s+1 (lnn)
2(s−2)(s+1) .
Consider the property As of having w-sat equal to w-sat(Gn,Ks).The property As ∩Bs is increasing with threshold r(n) ≥ q(n).
4. It remains to prove that, for n− 2s+1 ≤ p ≤ (1 + ε)q(n) the property
As does not hold a.a.s.
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![Page 44: Maksim Zhukovskii Moscow Institute of Physics and ......Maksim Zhukovskii Moscow Institute of Physics and Technology joint work with Mohamad Reza Bidgoli Combinatorics and geometry](https://reader035.fdocuments.us/reader035/viewer/2022071406/60fbcb00f972dc230513d2d3/html5/thumbnails/44.jpg)
Existence of the threshold
1. If p ≤ 1n lnn , then whp there are no Ks and the number of edges is
smaller than n2 .
2. If 1n lnn < p� n− 2
s+1 , then a.a.s. the number of Ks is smaller that12
(n2
)p.
3. Consider the property Bs that every pair of vertices have s− 2adjacent common neighbors.This property is increasing with sharp threshold probability
q(n) = (2(s− 2)!)2
(s+1)(s−2)n− 2s+1 (lnn)
2(s−2)(s+1) .
Consider the property As of having w-sat equal to w-sat(Gn,Ks).The property As ∩Bs is increasing with threshold r(n) ≥ q(n).
4. It remains to prove that, for n− 2s+1 ≤ p ≤ (1 + ε)q(n) the property
As does not hold a.a.s.
11 / 12
![Page 45: Maksim Zhukovskii Moscow Institute of Physics and ......Maksim Zhukovskii Moscow Institute of Physics and Technology joint work with Mohamad Reza Bidgoli Combinatorics and geometry](https://reader035.fdocuments.us/reader035/viewer/2022071406/60fbcb00f972dc230513d2d3/html5/thumbnails/45.jpg)
Existence of the threshold
1. If p ≤ 1n lnn , then whp there are no Ks and the number of edges is
smaller than n2 .
2. If 1n lnn < p� n− 2
s+1 , then a.a.s. the number of Ks is smaller that12
(n2
)p.
3. Consider the property Bs that every pair of vertices have s− 2adjacent common neighbors.This property is increasing with sharp threshold probability
q(n) = (2(s− 2)!)2
(s+1)(s−2)n− 2s+1 (lnn)
2(s−2)(s+1) .
Consider the property As of having w-sat equal to w-sat(Gn,Ks).The property As ∩Bs is increasing with threshold r(n) ≥ q(n).
4. It remains to prove that, for n− 2s+1 ≤ p ≤ (1 + ε)q(n) the property
As does not hold a.a.s.
11 / 12
![Page 46: Maksim Zhukovskii Moscow Institute of Physics and ......Maksim Zhukovskii Moscow Institute of Physics and Technology joint work with Mohamad Reza Bidgoli Combinatorics and geometry](https://reader035.fdocuments.us/reader035/viewer/2022071406/60fbcb00f972dc230513d2d3/html5/thumbnails/46.jpg)
Thank you!!
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