Random Graphs
[15/60%]If in a graph with vertices the minimum vertex degree equals , then
for any there exists a set of vertices such that the union of and the set of all vertices not joined to any vertex of contains at most vertices;
there exists a set of vertices such that every vertex of is joined by an edge to some vertex of and .
If for some , then (here are the Ramsey numbers, see Sec. 4.1).
(we write if ).
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?
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).)
For any integers there exists a graph that contains no cycles of length less than and cannot be properly colored in colors. (See the definition of a proper coloring in Sec. 3.1.)
For given and , the probability of having vertices with no edges between them is less than .
Fix and call the probability of a graph (in the Erdős--Rényi model, or probability space) with vertices and edges the number . The probability of a family (or, which is the same, a property) of graphs with vertices 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 .
For example, the number of edges of a graph is a random variable.
Let a random variable take distinct values . Then the mathematical expectation (expectation) of the random variable is its ``weighted average''
where is the set of all graphs for which . The latter probability is denoted by .
For given and , find the expectation of the number of
isolated vertices;
triangles;
-cliques;
-cliques that are connected components;
Hamiltonian cycles;
non-self-intersecting cycles of length ;
non-self-intersecting cycles of length that are connected components with exactly edges;
trees with vertices;
tree components of a given size , i.e., trees with vertices that are connected components.
For a given , find the asymptotics (for constant and ) of the function (i.e., of the -th factorial moment), if is the number of isolated vertices.
For given and , find the variance of the number of
isolated vertices;
triangles.
Prove that for any random variables and the following properties hold:
;
if and are independent (i.e., for any we have ).
Let be a random variable (as defined before Problem 6.3.4) and .
Markov’s inequality. . (Cf. Problem 6.3.3 (1).)
Chebyshev’s inequality. .
An event occurs asymptotically almost surely (or with asymptotic probability 1) with respect to a sequence if . A commonly used shorthand: for the event 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 occurs a.a.s.'' cannot be properly defined).
Recall that here is the number of vertices of the graph.
For
a.a.s.there are more than isolated vertices;
for some , a.a.s.every connected component has fewer than vertices (specialists say: fewer than vertices);
a.a.s.every connected component is a tree or a unicyclic graph;
for some , a.a.s.there are fewer than unicyclic components.
For , a.a.s.the edges are pairwise disjoint.
For and , there exists a function such that a.a.s.the number of vertices of degree 1 is greater than and less than , and the degrees of all the other vertices are zero.
If (), then for a.a.s.the random graph is connected (disconnected).
We state a result from [B, p.100, Theorem 5.4]. Let . For denote by the number of connected components of the random graph that are trees with vertices.
If , then a.a.s..
If , then the sequence of random variables converges as to a random variable having the Poisson distribution with parameter , i.e., for any , , we have .
If and , then for any .
If , then the sequence of random variables converges as to a random variable having the Poisson distribution with parameter .
If , then a.a.s..
Find at least one function such that
-
for , a.a.s.the graph contains no triangle,
-
for , a.a.s.the graph contains a triangle.
The same with the triangle replaced by a subgraph isomorphic to .
Such a function 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.
The chromatic number of the graph is a.a.s.at most
one for ;
two for ;
three for , where .
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 , a.a.s.(for ) errs by a factor of at most .
For any there exists a sequence of graphs with vertices such that, for a random numbering of the vertices of the graph (i.e., with the probability of each numbering equal to ), the probability that the ratio of the number of colors in the greedy coloring to is greater than is greater than . (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!)
See [R3, R4, R5] for more details. In particular, the following results are proved in [R4].
Bollobás’s first theorem. There exists a sequence for which, for , a.a.s.. (This theorem generalizes to practically all values of [JLR].)
Bollobás’s second theorem. For any there exist sequences and for which, for , a.a.s.. (In this theorem, for some the sequences and can be chosen so that .)