Section 6.3

Random Graphs

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Problem 6.3.1

If in a graph G=(V,E)G=(V,E) with nn vertices the minimum vertex degree equals δ\delta, then

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for any p∈(0,1)p\in (0,1) there exists a set of vertices A⊂VA\subset V such that the union of AA and the set of all vertices not joined to any vertex of AA contains at most np+n(1−p)δ+1np+n(1-p)^{\delta +1} vertices;

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there exists a set of vertices D⊂VD\subset V such that every vertex of V∖DV\setminus D is joined by an edge to some vertex of DD and ∣D∣⩽n1+ln⁡(δ+1)δ+1\left|D\right|\leqslant n\dfrac {1+\ln (\delta +1)}{\delta +1}.

Problem 6.3.2
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If (km)p(m2)+(kn)(1−p)(n2)<1\dbinom {k}{m}p^{\binom {m}{2}}+\dbinom {k}{n}(1-p)^{\binom {n}{2}} < 1 for some p∈(0,1)p\in (0,1), then R(m,n)>kR(m,n) > k (here R(m,n)R(m,n) are the Ramsey numbers, see Sec. 4.1).

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R(4,n)⩾Ω(n2ln⁡2n)R(4,n) \geqslant \Omega \left(\dfrac {n^2}{\ln^2 n}\right) (we write g⩾Ω(f)g \geqslant \Omega (f) if f=O(g)f=O(g)).

Problem 6.3.3
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Cherchez la femme. At a Russian-French meeting there were no representatives of other countries. The total amount of money held by the French turned out to be greater than the total amount of money held by the Russians, and the total amount of money held by the women turned out to be greater than the total amount of money held by the men. Must there have been a Frenchwoman at the meeting?

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Banknotes of different denominations and from different countries are packed into two suitcases. The average value of a banknote equals 100 rubles. The total number of banknotes in the left suitcase is greater than in the right one. Must there be a banknote worth at most 200 rubles in the left suitcase? (Cf. Markov's inequality 6.3.9 (1).)

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For any integers l,q>0l,q>0 there exists a graph that contains no cycles of length less than ll and cannot be properly colored in qq colors. (See the definition of a proper coloring in Sec. 3.1.)

Problem 6.3.4

For given nn and pp, the probability of having kk vertices with no edges between them is less than ekln⁡n−pk(k−1)/2e^{k\ln n - pk(k-1)/2}.

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Remark.
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Fix p∈(0,1)p\in (0,1) and call the probability of a graph (in the Erdős--Rényi model, or probability space) with nn vertices {1,2,…,n}\left\{ 1,2,\ldots ,n\right\} and rr edges the number P(G)=Pp(G):=pr(1−p)n(n−1)2−r\mathbb {P}\left(G\right)=\mathbb {P}_{p}\left(G\right) := p^r(1-p)^{\frac{n(n-1)}{2}-r}. The probability of a family (or, which is the same, a property) of graphs with vertices 1,2,…,n1,2,\ldots ,n is the sum of the probabilities of the graphs belonging to it.

A random variable is a function defined on the set of graphs with vertices 1,2,…,n1,2,\ldots ,n.

For example, the number of edges of a graph is a random variable.

Let a random variable YY take kk distinct values y1,…,yky_1,\ldots ,y_k. Then the mathematical expectation (expectation) of the random variable YY is its ``weighted average''

E[Y]:=∑s=1kysP(Y−1(ys)), \mathbb {E}\left[Y\right] := \sum _{s=1}^{k}y_s\mathbb {P}\left(Y^{-1}(y_s)\right),

where Y−1(ys)Y^{-1}(y_s) is the set of all graphs GG for which Y(G)=ysY(G)=y_s. The latter probability is denoted by P(Y=ys)\mathbb {P}\left(Y=y_s\right).

Problem 6.3.5

For given nn and pp, find the expectation of the number of

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isolated vertices;

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triangles;

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kk-cliques;

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kk-cliques that are connected components;

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Hamiltonian cycles;

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non-self-intersecting cycles of length kk;

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non-self-intersecting cycles of length kk that are connected components with exactly kk edges;

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trees with kk vertices;

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tree components of a given size kk, i.e., trees with kk vertices that are connected components.

Problem 6.3.6

For a given pp, find the asymptotics (for constant kk and n→∞n\to \infty) of the function E(k)[Y]:=E[Y(Y−1)…(Y−k+1)]\mathbb {E}_{(k)}\left[Y\right] := \mathbb {E}\left[Y(Y-1)\ldots (Y-k+1)\right] (i.e., of the kk-th factorial moment), if YY is the number of isolated vertices.

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Problem 6.3.7

For given nn and pp, find the variance of the number of

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isolated vertices;

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triangles.

Problem 6.3.8

Prove that for any random variables XX and YY the following properties hold:

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E[X+Y]=E[X]+E[Y]\mathbb {E}\left[X+Y\right] = \mathbb {E}\left[X\right]+\mathbb {E}\left[Y\right];

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Var⁡[X+Y]=Var⁡[X]+Var⁡[Y]\operatorname {Var}\left[X+Y\right] = \operatorname {Var}\left[X\right]+\operatorname {Var}\left[Y\right] if XX and YY are independent (i.e., for any x,yx,y we have P(X=x,Y=y)=P(X=x)⋅P(Y=y)\mathbb {P}\left(X=x,Y=y\right)=\mathbb {P}\left(X=x\right)\cdot \mathbb {P}\left(Y=y\right)).

Problem 6.3.9

Let XX be a random variable (as defined before Problem 6.3.4) and a>0a>0.

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Markov’s inequality. P(∣X∣>a)⩽E[∣X∣]/a\mathbb {P}\left(\left|X\right|>a\right) \leqslant \mathbb {E}\left[\left|X\right|\right]/a. (Cf. Problem 6.3.3 (1).)

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Chebyshev’s inequality. P(∣X−E[X]∣>a)⩽Var⁡[X]/a2\mathbb {P}\left(\left|X-\mathbb {E}\left[X\right]\right|>a\right) \leqslant \operatorname {Var}\left[X\right]/a^2.

An event AnA_n occurs asymptotically almost surely (or with asymptotic probability 1) with respect to a sequence f(n)f(n) if Pf(n)(An)→1\mathbb {P}_{f(n)}\left(A_n\right) \to 1. A commonly used shorthand: for p(n)=f(n)p(n)=f(n) the event AnA_n occurs a.a.s. (formally, this phrase makes no sense, since it means if $p(n)=f(n)$, then the event $A_n$ occurs a.a.s.'', while without specifying the sequence $f(n)$ the phrase the event AnA_n occurs a.a.s.'' cannot be properly defined).

Recall that here nn is the number of vertices of the graph.

Problem 6.3.10

For p(n)=1/(2n)p(n)=1/(2n)

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a.a.s.there are more than n/2n/2 isolated vertices;

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for some C>0C>0, a.a.s.every connected component has fewer than Cln⁡nC\ln n vertices (specialists say: fewer than O(ln⁡n)O(\ln n) vertices);

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a.a.s.every connected component is a tree or a unicyclic graph;

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for some C>0C>0, a.a.s.there are fewer than CC unicyclic components.

Problem 6.3.11
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For p(n)=o(n−3/2)p(n)=o(n^{-3/2}), a.a.s.the edges are pairwise disjoint.

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For p=p(n)=o(n−3/2)p=p(n)=o(n^{-3/2}) and pn2→∞pn^2\to \infty, there exists a function r=r(n)=o(pn2)r=r(n)=o(pn^2) such that a.a.s.the number of vertices of degree 1 is greater than pn2−rpn^2-r and less than pn2+rpn^2+r, and the degrees of all the other vertices are zero.

Problem 6.3.12

If c>1c>1 (0<c<10<c<1), then for p(n)=cln⁡n/np(n)=c\ln n/n a.a.s.the random graph is connected (disconnected).

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Remark.
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We state a result from [B, p.100, Theorem 5.4]. Let pn=p(n)p_n=p(n). For k⩾2k\geqslant 2 denote by Tk=Tk,pnT_k=T_{k,p_n} the number of connected components of the random graph that are trees with kk vertices.

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If pn=o(n−k/(k−1))p_n=o(n^{-k/(k-1)}), then a.a.s.Tk=0T_k=0.

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If lim⁡n→∞pnnk/(k−1)=c>0\lim_{n\to \infty }p_nn^{k/(k-1)}=c>0, then the sequence of random variables Tk=Tk,pnT_k=T_{k,p_n} converges as n→∞n\to \infty to a random variable having the Poisson distribution with parameter λ:=ck−1kk−2/k!\lambda := c^{k-1}k^{k-2}/k!, i.e., for any s∈Zs\in \mathbb {Z}, s⩾0s\geqslant 0, we have lim⁡n→∞P(Tk=s)=λss!e−λ\lim_{n\to \infty }\mathbb {P}\left(T_k=s\right)=\dfrac {\lambda^s}{s!}e^{-\lambda }.

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If nk/(k−1)=o(pn)n^{k/(k-1)}=o(p_n) and lim⁡n→∞(pnkn−ln⁡n−(k−1)ln⁡ln⁡n)=−∞\lim_{n\to \infty }(p_nkn-\ln n-(k-1)\ln \ln n)=-\infty, then lim⁡n→∞P(Tk⩾L)=1\lim_{n\to \infty }\mathbb {P}\left(T_k\geqslant L\right)=1 for any L>0L>0.

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If lim⁡n→∞(pnkn−ln⁡n−(k−1)ln⁡ln⁡n)=x∈R\lim_{n\to \infty }(p_nkn-\ln n-(k-1)\ln \ln n)=x\in \mathbb {R}, then the sequence of random variables Tk=Tk,pnT_k=T_{k,p_n} converges as n→∞n\to \infty to a random variable having the Poisson distribution with parameter λ:=e−x/(k⋅k!)\lambda := e^{-x}/(k\cdot k!).

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If lim⁡n→∞(pnkn−ln⁡n−(k−1)ln⁡ln⁡n)=+∞\lim_{n\to \infty }(p_nkn-\ln n-(k-1)\ln \ln n)=+\infty, then a.a.s.Tk=0T_k=0.

Problem 6.3.13
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Find at least one function p∗(n)p^*(n) such that

  • for p(n)/p∗(n)→0p(n)/p^*(n)\to 0, a.a.s.the graph contains no triangle,

  • for p(n)/p∗(n)→+∞p(n)/p^*(n)\to +\infty, a.a.s.the graph contains a triangle.

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The same with the triangle replaced by a subgraph isomorphic to K4K_4.

Remark.
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Such a function p∗p^* is called a threshold probability. A threshold probability exists for every monotone family of graphs. A monotonically increasing (decreasing) family of graphs is a family of graphs that, together with each graph, contains every supergraph (subgraph) of it.

Problem 6.3.14

The chromatic number of the graph is a.a.s.at most

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one for p(n)=o(1/n2)p(n)=o(1/n^2);

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two for p(n)=o(1/n)p(n)=o(1/n);

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three for p(n)=c/np(n)=c/n, where c<1c<1.

Problem 6.3.15
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The greedy coloring algorithm (see Problem 3.2.3: the vertices of the graph are processed in some order, and each of them is assigned the smallest color that does not occur among its already colored neighbors), for any positive ε\varepsilon, a.a.s.(for p(n)=1/2p(n)=1/2) errs by a factor of at most 2+ε2+\varepsilon.

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For any ε,δ>0\varepsilon ,\delta >0 there exists a sequence GnG_n of graphs with nn vertices such that, for a random numbering of the vertices of the graph GnG_n (i.e., with the probability of each numbering equal to 1/21/2), the probability that the ratio of the number of colors in the greedy coloring to χ(Gn)\chi (G_n) is greater than n1−εn^{1-\varepsilon } is greater than δ\delta. (In other words, on the one hand, for almost every graph the greedy coloring is good in any numbering, but, on the other hand, there are graphs for which, almost no matter how you number them, the result is still rubbish!)

Remark.
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See [R3, R4, R5] for more details. In particular, the following results are proved in [R4].

Bollobás’s first theorem. There exists a sequence fn=o(n2log⁡2n)f_n = o\left(\dfrac {n}{2\log_2 n}\right) for which, for p(n)=1/2p(n) = 1/2, a.a.s.∣χ(G)−n2log⁡2n∣<fn\left|\chi (G) - \dfrac {n}{2\log_2 n}\right| < f_n. (This theorem generalizes to practically all values of pp [JLR].)

Bollobás’s second theorem. For any α>2/3\alpha > 2/3 there exist sequences ana_n and bnb_n for which, for p(n)=n−αp(n) = n^{-\alpha }, a.a.s.χ(G)∈{an,bn}\chi (G) \in \left\{ a_n, b_n\right\}. (In this theorem, for some α\alpha the sequences ana_n and bnb_n can be chosen so that lim⁡n→∞an=lim⁡n→∞bn=∞\lim_{n \to \infty } a_n = \lim_{n \to \infty } b_n = \infty.)