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2015, Electronic Notes in Discrete Mathematics
A graph G is said to be H(n, ∆)-universal if it contains every graph on at most n vertices with maximum degree at most ∆. It is known that for any ε > 0 and any natural number ∆ there exists c > 0 such that the random graph G(n, p) is asymptotically almost surely H((1 − ε)n, ∆)universal for p ≥ c(log n/n) 1/∆. Bypassing this natural boundary, we show that for ∆ ≥ 3 the same conclusion holds when p ≫ n − 1 ∆−1 log 5 n.
Random Structures & Algorithms, 2011
We prove that for fixed integer D and positive reals α and γ, there exists a constant C 0 such that for all p satisfying p(n) ≥ C 0 /n, the random graph G(n, p) asymptotically almost surely contains a copy of every tree with maximum degree at most D and at most (1 − α)n vertices, even after we delete a (1/2 − γ)-fraction of the edges incident to each vertex. The proof uses Szemerédi's regularity lemma for sparse graphs and a bipartite variant of the theorem of Friedman and Pippenger on embedding bounded degree trees into expanding graphs.
Zeitschrift für Naturforschung A, 2012
A common property of many, though not all, massive real-world networks, including the World- Wide Web, the Internet, and social networks, is that the connectivity of the various nodes follows a scale-free distribution, P(k) ∞ k
Discrete Mathematics, 2007
Given a graph G and field F, the well-covered vector space of G is the vector space of all functions f :
Random Structures & Algorithms, 1995
Given a sequence of nonnegative real numbers λ0, λ1… which sum to 1, we consider random graphs having approximately λi n vertices of degree i. Essentially, we show that if Σ i(i ‐ 2)λi > 0, then such graphs almost surely have a giant component, while if Σ i(i ‐2)λ. < 0, then almost surely all components in such graphs are small. We can apply these results to Gn,p,Gn.M, and other well‐known models of random graphs. There are also applications related to the chromatic number of sparse random graphs.
The inductive dimension dim(G) of a finite undirected graph G is a rational number defined inductively as 1 plus the arithmetic mean of the dimensions of the unit spheres dim(S(x)) at vertices x primed by the requirement that the empty graph has dimension −1. We look at the distribution of the random variable dim on the Erdös-Rényi probability space G(n, p), where each of the n(n − 1)/2 edges appears independently with probability 0 ≤ p ≤ 1. We show that the average dimension dn(p) = Ep,n[dim] is a computable polynomial of degree n(n − 1)/2 in p. The explicit formulas allow experimentally to explore limiting laws for the dimension of large graphs. In this context of random graph geometry, we mention explicit formulas for the expectation Ep,n[χ] of the Euler characteristic χ, considered as a random variable on G(n, p). We look experimentally at the statistics of curvature K(v) and local dimension dim(v) = 1 + dim(S(v)) which satisfy χ(G) = v∈V K(v) and dim(G) = 1 |V | v∈V dim(v). We also look at the signature functions f (p) = Ep[dim], g(p) = Ep[χ] and matrix values functions Av,w(p) = Covp[dim(v), dim(w)], Bv,w(p) = Cov[K(v), K(w)] on the probability space G(p) of all subgraphs of a host graph G = (V, E) with the same vertex set V , where each edge is turned on with probability p.
Applied Mathematics Letters, 2011
For an ordered set W = {w 1 , w 2 , . . . , w k } of vertices and a vertex v in a connected graph G, the ordered k-vector r(v|W ) := (d(v, w 1 ), d(v, w 2 ), . . . , d(v, w k )) is called the (metric) representation of v with respect to W , where d(x, y) is the distance between the vertices x and y. The set W is called a resolving set for G if distinct vertices of G have distinct representations with respect to W . A resolving set for G with minimum cardinality is called a basis of G and its cardinality is the metric dimension of G. A connected graph G is called randomly k-dimensional graph if each k-set of vertices of G is a basis of G. In this paper, we study randomly k-dimensional graphs and provide some properties of these graphs.
Combinatorica, 2002
In this paper we describe a simple model for random graphs that have an n-fold covering map onto a fixed finite base graph. Roughly, given a base graph G and an integer n, we form a random graph by replacing each vertex of G by a set of n vertices, and joining these sets by random matchings whenever the corresponding vertices are adjacent in G. The resulting graph covers the original graph in the sense that the two are locally isomorphic. We suggest possible applications of the model, such as constructing graphs with extremal properties in a more controlled fashion than offered by the standard random models, and also "randomizing" given graphs. The main specific result that we prove here (Theorem 1) is that if δ ≥ 3 is the smallest vertex degree in G, then almost all n-covers of G are δconnected. In subsequent papers we will address other graph properties, such as girth, expansion and chromatic number.
Random Structures and Algorithms, 2001
Random d-regular graphs have been well studied when d is fixed and the number of vertices goes to infinity. We obtain results on many of the properties of a random d-regular graph when d = d n grows more quickly than √ n. These properties include connectivity, hamiltonicity, independent set size, chromatic number, choice number, and the size of the second eigenvalue, among others.
Random Structures and Algorithms, 2005
We study random subgraphs of an arbitrary finite connected transitive graph G obtained by independently deleting edges with probability 1 − p. Let V be the number of vertices in G, and let Ω be their degree. We define the critical threshold p c = p c (G, λ) to be the value of p for which the expected cluster size of a fixed vertex attains the value λV 1/3 , where λ is fixed and positive. We show that for any such model, there is a phase transition at p c analogous to the phase transition for the random graph, provided that a quantity called the triangle diagram is sufficiently small at the threshold p c . In particular, we show that the largest cluster inside a scaling window of size |p − p c | = Θ(Ω −1 V −1/3 ) is of size Θ(V 2/3 ), while below this scaling window, it is much smaller, of order O( −2 log(V 3 )), with = Ω(p c − p). We also obtain an upper bound O(Ω(p − p c )V ) for the expected size of the largest cluster above the window. In addition, we define and analyze the percolation probability above the window and show that it is of order Θ(Ω(p − p c )). Among the models for which the triangle diagram is small enough to allow us to draw these conclusions are the random graph, the n-cube and certain Hamming cubes, as well as the spread-out n-dimensional torus for n > 6.
SIAM Journal on Discrete Mathematics, 2015
2015
This thesis provides an introduction to the fundamentals of random graph theory. The study starts introduces the two fundamental building blocks of random graph theory, namely discrete probability and graph theory. The study starts by introducing relevant concepts probability commonly used in random graph theory-these include concentration inequalities such as Chebyshev's inequality and Chernoff's inequality. Moreover we proceed by introducing central concepts in graph theory, which will underpin the later discussion. In particular we provide results such as Mycielski's construction of a family of triangle-free graphs with high chromatic number and results in Ramsey theory. Next we introduce the concept of a random graph and present two of the most famous proofs in graph theory using the theory random graphs. These include the proof of the fact that there are graphs with arbitrarily high girth and chromatic number, and a bound on the Ramsey number R(k, k). Finally we conclude by introducing the notion of a threshold function for a monotone graph property and we present proofs for the threhold functions of certain properties.
Arxiv preprint math.PR/0502580
In this paper we derive results concerning the connected components and the diameter of random graphs with an arbitrary iid degree sequence. We study these properties primarily, but not exclusively, when the tail of the degree ...
SIAM Journal on Computing, 2010
We deal with two intimately related subjects: quasi-randomness and regular partitions. The purpose of the concept of quasi-randomness is to measure how much a given graph "resembles" a random one. Moreover, a regular partition approximates a given graph by a bounded number of quasi-random graphs. Regarding quasirandomness, we present a new spectral characterization of low discrepancy, which extends to sparse graphs. Concerning regular partitions, we introduce a concept of regularity that takes into account vertex weights, and show that if G = (V, E) satisfies a certain boundedness condition, then G admits a regular partition. In addition, building on the work of Alon and Naor [4], we provide an algorithm that computes a regular partition of a given (possibly sparse) graph G in polynomial time. As an application, we present a polynomial time approximation scheme for MAX CUT on (sparse) graphs without "dense spots".
Bulletin of the London Mathematical Society, 2017
In this paper we consider the problem of embedding almost-spanning, bounded degree graphs in a random graph. In particular, let ∆ ≥ 5, ε > 0 and let H be a graph on (1 − ε)n vertices and with maximum degree ∆. We show that a random graph G n,p with high probability contains a copy of H, provided that p (n −1 log 1/∆ n) 2/(∆+1). Our assumption on p is optimal up to the polylog factor. We note that this polylog term matches the conjectured threshold for the spanning case.
Jct, 2007
The generalized Turán number ex(G, H) of two graphs G and H is the maximum number of edges in a subgraph of G not containing H. When G is the complete graph Km on m vertices, the value of ex(Km, H) is (1−1/(χ(H)−1)+o )`m 2´, where o(1) → 0 as m → ∞, by the Erdős-Stone-Simonovits Theorem.
2011
Abstract. The inductive dimension dim(G) of a finite undirected graph G is a rational number defined inductively as 1 plus the arithmetic mean of the dimensions of the unit spheres dim(S(x)) at vertices x primed by the requirement that the empty graph has dimension −1. We look at the dis-tribution of the random variable dim on the Erdös-Rényi probability space G(n, p), where each of the n(n − 1)/2 edges appears independently with prob-ability 0 ≤ p ≤ 1. We show that the average dimension dn(p) = Ep,n[dim] is a computable polynomial of degree n(n − 1)/2 in p. The explicit formu-las allow experimentally to explore limiting laws for the dimension of large graphs. In this context of random graph geometry, we mention explicit for-mulas for the expectation Ep,n[χ] of the Euler characteristic χ, considered as a random variable on G(n, p). We look experimentally at the statistics of curvature K(v) and local dimension dim(v) = 1 + dim(S(v)) which satisfy χ(G) = v∈V K(v) and dim(G) =
International Mathematics Research Notices, 2015
For a fixed number d > 0 and n large, let G(n, d/n) be the random graph on n vertices in which any two vertices are connected with probability d/n independently. The problem of determining the chromatic number of G(n, d/n) goes back to the famous 1960 article of Erdős and Rényi that started the theory of random graphs [Magayar Tud. Akad. Mat. Kutato Int. Kozl. 5 (1960) 17-61]. Progress culminated in the landmark paper of Achlioptas and Naor [Ann. Math. 162 (2005) 1333-1349], in which they calculate the chromatic number precisely for all d in a set S ⊂ (0, ∞) of asymptotic density lim z→∞ 1 z z 0 1 S = 1 2 , and up to an additive error of one for the remaining d. Here we obtain a near-complete answer by determining the chromatic number of G(n, d/n) for all d in a set of asymptotic density 1.
Combinatorics, Probability and Computing, 2007
Let G be a graph with n vertices, and let k be an integer dividing n. G is said to be strongly k-colorable if for every partition of V (G) into disjoint sets V 1 ∪. .. ∪ V r , all of size exactly k, there exists a proper vertex k-coloring of G with each color appearing exactly once in each V i. In the case when k does not divide n, G is defined to be strongly k-colorable if the graph obtained by adding k n k − n isolated vertices is strongly k-colorable. The strong chromatic number of G is the minimum k for which G is strongly k-colorable. In this paper, we study the behavior of this parameter for the random graph G n,p. In the dense case when p ≫ n −1/3 , we prove that the strong chromatic number is a.s. concentrated on one value ∆ + 1, where ∆ is the maximum degree of the graph. We also obtain several weaker results for sparse random graphs.
The Electronic Journal of Combinatorics, 2015
We show how to adjust a very nice coupling argument due to McDiarmid in order to prove/reprove in a novel way results concerning Hamilton cycles in various models of random graph and hypergraphs. In particular, we firstly show that for $k\geq 3$, if $pn^{k-1}/\log n$ tends to infinity, then a random $k$-uniform hypergraph on $n$ vertices, with edge probability $p$, with high probability (w.h.p.) contains a loose Hamilton cycle, provided that $(k-1)|n$. This generalizes results of Frieze, Dudek and Frieze, and reproves a result of Dudek, Frieze, Loh and Speiss. Secondly, we show that there exists $K>0$ such for every $p\geq (K\log n)/n$ the following holds: Let $G_{n,p}$ be a random graph on $n$ vertices with edge probability $p$, and suppose that its edges are being colored with $n$ colors uniformly at random. Then, w.h.p. the resulting graph contains a Hamilton cycle with for which all the colors appear (a rainbow Hamilton cycle). Bal and Frieze proved the latter statement for g...
Journal of Combinatorial Theory, Series A, 2007
The generalized Turán number ex(G, H) of two graphs G and H is the maximum number of edges in a subgraph of G not containing H. When G is the complete graph Km on m vertices, the value of ex(Km, H) is (1−1/(χ(H)−1)+o )`m 2´, where o(1) → 0 as m → ∞, by the Erdős-Stone-Simonovits Theorem.
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