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On the Cycle Double Cover Problem
On The Cycle Double Cover Problem Ali Ghassâb1 Dedicated to Prof. E.S. Mahmoodian Abstract In this paper, for each graph , a free edge set is defined. To study the existence of cycle double cover, the naïve cycle double cover of have been defined and studied. In the main theorem, the paper, based on the Kuratowski minor properties, presents a condition to guarantee the existence of a naïve cycle double cover for couple . As a result, the cycle double cover conjecture has been concluded. Moreover, Goddyn’s conjecture - asserting if is a cycle in bridgeless graph , there is a cycle double cover of containing - will have been proved. 1 Ph.D. student at Sharif University of Technology e-mail: [email protected] Faculty of Math, Sharif University of Technology, Tehran, Iran 1 Cycle Double Cover: History, Trends, Advantages A cycle double cover of a graph is a collection of its cycles covering each edge of the graph exactly twice. G. Szekeres in 1973 and, independently, P. Seymour in 1979 conjectured: Conjecture (cycle double cover). Every bridgeless graph has a cycle double cover. Yielded next data are just a glimpse review of the history, trend, and advantages of the research. There are three extremely helpful references: F. Jaeger’s survey article as the oldest one, and M. Chan’s survey article as the newest one. Moreover, C.Q. Zhang’s book as a complete reference illustrating the relative problems and rather new researches on the conjecture. A number of attacks, to prove the conjecture, have been happened. Some of them have built new approaches and trends to study. -
Information Theory
Information Theory Gurkirat,Harsha,Parth,Puneet,Rahul,Tushant November 8, 2015 1 Introduction[1] Information theory is a branch of applied mathematics, electrical engineering, and computer science involving the quantification of information . Firstly, it provides a general way to deal with any information , be it language, music, binary codes or pictures by introducing a measure of information , en- tropy as the average number of bits(binary for our convenience) needed to store one symbol in a message. This abstraction of information through entropy allows us to deal with all kinds of information mathematically and apply the same principles without worrying about it's type or form! It is remarkable to note that the concept of entropy was introduced by Ludwig Boltzmann in 1896 as a measure of disorder in a thermodynamic system , but serendipitously found it's way to become a basic concept in Information theory introduced by Shannon in 1948 . Information theory was developed as a way to find fundamental limits on the transfer , storage and compression of data and has found it's way into many applications including Data Compression and Channel Coding. 2 Basic definitions 2.1 Intuition behind "surprise" or "information"[2] Suppose someone tells you that the sun will come up tomorrow.This probably isn't surprising. You already knew the sun will come up tomorrow, so they didn't't really give you much information. Now suppose someone tells you that aliens have just landed on earth and have picked you to be the spokesperson for the human race. This is probably pretty surprising, and they've given you a lot of information.The rarer something is, the more you've learned if you discover that it happened. -
Large Girth Graphs with Bounded Diameter-By-Girth Ratio 3
LARGE GIRTH GRAPHS WITH BOUNDED DIAMETER-BY-GIRTH RATIO GOULNARA ARZHANTSEVA AND ARINDAM BISWAS Abstract. We provide an explicit construction of finite 4-regular graphs (Γk)k∈N with girth Γk k → ∞ as k and diam Γ 6 D for some D > 0 and all k N. For each dimension n > 2, we find a → ∞ girth Γk ∈ pair of matrices in SLn(Z) such that (i) they generate a free subgroup, (ii) their reductions mod p generate SLn(Fp) for all sufficiently large primes p, (iii) the corresponding Cayley graphs of SLn(Fp) have girth at least cn log p for some cn > 0. Relying on growth results (with no use of expansion properties of the involved graphs), we observe that the diameter of those Cayley graphs is at most O(log p). This gives infinite sequences of finite 4-regular Cayley graphs as in the title. These are the first explicit examples in all dimensions n > 2 (all prior examples were in n = 2). Moreover, they happen to be expanders. Together with Margulis’ and Lubotzky-Phillips-Sarnak’s classical con- structions, these new graphs are the only known explicit large girth Cayley graph expanders with bounded diameter-by-girth ratio. 1. Introduction The girth of a graph is the edge-length of its shortest non-trivial cycle (it is assigned to be infinity for an acyclic graph). The diameter of a graph is the greatest edge-length distance between any pair of its vertices. We regard a graph Γ as a sequence of its connected components Γ = (Γk)k∈N each of which is endowed with the edge-length distance. -
Girth in Graphs
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector JOURNAL OF COMBINATORIAL THEORY. Series B 35, 129-141 (1983) Girth in Graphs CARSTEN THOMASSEN Mathematical institute, The Technicai University oJ Denmark, Building 303, Lyngby DK-2800, Denmark Communicated by the Editors Received March 31, 1983 It is shown that a graph of large girth and minimum degree at least 3 share many properties with a graph of large minimum degree. For example, it has a contraction containing a large complete graph, it contains a subgraph of large cyclic vertex- connectivity (a property which guarantees, e.g., that many prescribed independent edges are in a common cycle), it contains cycles of all even lengths module a prescribed natural number, and it contains many disjoint cycles of the same length. The analogous results for graphs of large minimum degree are due to Mader (Math. Ann. 194 (1971), 295-312; Abh. Math. Sem. Univ. Hamburg 31 (1972), 86-97), Woodall (J. Combin. Theory Ser. B 22 (1977), 274-278), Bollobis (Bull. London Math. Sot. 9 (1977), 97-98) and Hlggkvist (Equicardinal disjoint cycles in sparse graphs, to appear). Also, a graph of large girth and minimum degree at least 3 has a cycle with many chords. An analogous result for graphs of chromatic number at least 4 has been announced by Voss (J. Combin. Theory Ser. B 32 (1982), 264-285). 1, INTRODUCTION Several authors have establishedthe existence of various configurations in graphs of sufficiently large connectivity (independent of the order of the graph) or, more generally, large minimum degree (see, e.g., [2, 91). -
Computing the Girth of a Planar Graph in Linear Time∗
Computing the Girth of a Planar Graph in Linear Time∗ Hsien-Chih Chang† Hsueh-I Lu‡ April 22, 2013 Abstract The girth of a graph is the minimum weight of all simple cycles of the graph. We study the problem of determining the girth of an n-node unweighted undirected planar graph. The first non-trivial algorithm for the problem, given by Djidjev, runs in O(n5/4 log n) time. Chalermsook, Fakcharoenphol, and Nanongkai reduced the running time to O(n log2 n). Weimann and Yuster further reduced the running time to O(n log n). In this paper, we solve the problem in O(n) time. 1 Introduction Let G be an edge-weighted simple graph, i.e., G does not contain multiple edges and self- loops. We say that G is unweighted if the weight of each edge of G is one. A cycle of G is simple if each node and each edge of G is traversed at most once in the cycle. The girth of G, denoted girth(G), is the minimum weight of all simple cycles of G. For instance, the girth of each graph in Figure 1 is four. As shown by, e.g., Bollobas´ [4], Cook [12], Chandran and Subramanian [10], Diestel [14], Erdos˝ [21], and Lovasz´ [39], girth is a fundamental combinatorial characteristic of graphs related to many other graph properties, including degree, diameter, connectivity, treewidth, and maximum genus. We address the problem of computing the girth of an n-node graph. Itai and Rodeh [28] gave the best known algorithm for the problem, running in time O(M(n) log n), where M(n) is the time for multiplying two n × n matrices [13]. -
Probabilistic Methods in Combinatorics
Lecture notes (MIT 18.226, Fall 2020) Probabilistic Methods in Combinatorics Yufei Zhao Massachusetts Institute of Technology [email protected] http://yufeizhao.com/pm/ Contents 1 Introduction7 1.1 Lower bounds to Ramsey numbers......................7 1.1.1 Erdős’ original proof..........................8 1.1.2 Alteration method...........................9 1.1.3 Lovász local lemma........................... 10 1.2 Set systems................................... 11 1.2.1 Sperner’s theorem............................ 11 1.2.2 Bollobás two families theorem..................... 12 1.2.3 Erdős–Ko–Rado theorem on intersecting families........... 13 1.3 2-colorable hypergraphs............................ 13 1.4 List chromatic number of Kn;n ......................... 15 2 Linearity of expectations 17 2.1 Hamiltonian paths in tournaments....................... 17 2.2 Sum-free set................................... 18 2.3 Turán’s theorem and independent sets.................... 18 2.4 Crossing number inequality.......................... 20 2.4.1 Application to incidence geometry................... 22 2.5 Dense packing of spheres in high dimensions................. 23 2.6 Unbalancing lights............................... 26 3 Alterations 28 3.1 Ramsey numbers................................ 28 3.2 Dominating set in graphs............................ 28 3.3 Heilbronn triangle problem........................... 29 3.4 Markov’s inequality............................... 31 3.5 High girth and high chromatic number.................... 31 3.6 Greedy random coloring............................ 32 2 4 Second moment method 34 4.1 Threshold functions for small subgraphs in random graphs......... 36 4.2 Existence of thresholds............................. 42 4.3 Clique number of a random graph....................... 47 4.4 Hardy–Ramanujan theorem on the number of prime divisors........ 49 4.5 Distinct sums.................................. 52 4.6 Weierstrass approximation theorem...................... 54 5 Chernoff bound 56 5.1 Discrepancy.................................. -
The Line Mycielskian Graph of a Graph
[VOLUME 6 I ISSUE 1 I JAN. – MARCH 2019] e ISSN 2348 –1269, Print ISSN 2349-5138 http://ijrar.com/ Cosmos Impact Factor 4.236 THE LINE MYCIELSKIAN GRAPH OF A GRAPH Keerthi G. Mirajkar1 & Veena N. Mathad2 1Department of Mathematics, Karnatak University's Karnatak Arts College, Dharwad - 580001, Karnataka, India 2Department of Mathematics, University of Mysore, Manasagangotri, Mysore-06, India Received: January 30, 2019 Accepted: March 08, 2019 ABSTRACT: In this paper, we introduce the concept of the line mycielskian graph of a graph. We obtain some properties of this graph. Further we characterize those graphs whose line mycielskian graph and mycielskian graph are isomorphic. Also, we establish characterization for line mycielskian graphs to be eulerian and hamiltonian. Mathematical Subject Classification: 05C45, 05C76. Key Words: Line graph, Mycielskian graph. I. Introduction By a graph G = (V,E) we mean a finite, undirected graph without loops or multiple lines. For graph theoretic terminology, we refer to Harary [3]. For a graph G, let V(G), E(G) and L(G) denote the point set, line set and line graph of G, respectively. The line degree of a line uv of a graph G is the sum of the degree of u and v. The open-neighborhoodN(u) of a point u in V(G) is the set of points adjacent to u. For each ui of G, a new point uˈi is introduced and the resulting set of points is denoted by V1(G). Mycielskian graph휇(G) of a graph G is defined as the graph having point set V(G) ∪V1(G) ∪v and line set E(G) ∪ {xyˈ : xy∈E(G)} ∪ {y ˈv : y ˈ ∈V1(G)}. -
Lecture 13: July 18, 2013 Prof
UChicago REU 2013 Apprentice Program Spring 2013 Lecture 13: July 18, 2013 Prof. Babai Scribe: David Kim Exercise 0.1 * If G is a regular graph of degree d and girth ≥ 5, then n ≥ d2 + 1. Definition 0.2 (Girth) The girth of a graph is the length of the shortest cycle in it. If there are no cycles, the girth is infinite. Example 0.3 The square grid has girth 4. The hexagonal grid (honeycomb) has girth 6. The pentagon has girth 5. Another graph of girth 5 is two pentagons sharing an edge. What is a very famous graph, discovered by the ancient Greeks, that has girth 5? Definition 0.4 (Platonic Solids) Convex, regular polyhedra. There are 5 Platonic solids: tetra- hedron (four faces), cube (\hexahedron" - six faces), octahedron (eight faces), dodecahedron (twelve faces), icosahedron (twenty faces). Which of these has girth 5? Can you model an octahedron out of a cube? Hint: a cube has 6 faces, 8 vertices, and 12 edges, while an octahedron has 8 faces, 6 vertices, 12 edges. Definition 0.5 (Dual Graph) The dual of a plane graph G is a graph that has vertices corre- sponding to each face of G, and an edge joining two neighboring faces for each edge in G. Check that the dual of an dodecahedron is an icosahedron, and vice versa. Definition 0.6 (Tree) A tree is a connected graph with no cycles. Girth(tree) = 1. For all other connected graphs, Girth(G) < 1. Definition 0.7 (Bipartite Graph) A graph is bipartite if it is colorable by 2 colors, or equiva- lently, if the vertices can be partitioned into two independent sets. -
14 the Probabilistic Method
14 The Probabilistic Method Probabilistic Graph Theory Theorem 14.1 (Szele) For every positive integer n, there exists a tournament on n vertices with at least n!2¡(n¡1) Hamiltonian paths. Proof: Construct a tournament by randomly orienting each edge of Kn in each direction 1 independently with probability 2 . For any permutation σ of the vertices, let Xσ be the indicator random variable which has value 1 if σ is the vertex sequence of a Hamiltonian path, and 0 otherwise. Let X be the random variable which is the total number of Hamiltonain P paths. Then X = Xσ and we have X ¡(n¡1) E(X) = E(Xσ) = n!2 : σ Thus, there must exist at least one tournament on n vertices which has ¸ n!2¡(n¡1) Hamil- tonain paths as claimed. ¤ 1 Theorem 14.2 Every graph G has a bipartite subgraph H for which jE(H)j ¸ 2 jE(G)j. Proof: Choose a random subset S ⊆ V (G) by independently choosing each vertex to be in S 1 with probability 2 . Let H be the subgraph of G containing all of the vertices, and all edges with exactly one end in S. For every edge e, let Xe be the indicator random variable with value 1 if e 2 E(H) and 0 otherwise. If e = uv, then e will be in H if u 2 S and v 62 S or if 1 u 62 S and v 2 S, so E(Xe) = P(e 2 E(H)) = 2 and we ¯nd X 1 E(jE(H)j) = E(Xe) = 2 jE(G)j: e2E(G) 1 Thus, there must exist at least one bipartite subgraph H with jE(H)j ¸ 2 jE(G)j. -
Dynamic Cage Survey
Dynamic Cage Survey Geoffrey Exoo Department of Mathematics and Computer Science Indiana State University Terre Haute, IN 47809, U.S.A. [email protected] Robert Jajcay Department of Mathematics and Computer Science Indiana State University Terre Haute, IN 47809, U.S.A. [email protected] Department of Algebra Comenius University Bratislava, Slovakia [email protected] Submitted: May 22, 2008 Accepted: Sep 15, 2008 Version 1 published: Sep 29, 2008 (48 pages) Version 2 published: May 8, 2011 (54 pages) Version 3 published: July 26, 2013 (55 pages) Mathematics Subject Classifications: 05C35, 05C25 Abstract A(k; g)-cage is a k-regular graph of girth g of minimum order. In this survey, we present the results of over 50 years of searches for cages. We present the important theorems, list all the known cages, compile tables of current record holders, and describe in some detail most of the relevant constructions. the electronic journal of combinatorics (2013), #DS16 1 Contents 1 Origins of the Problem 3 2 Known Cages 6 2.1 Small Examples . 6 2.1.1 (3,5)-Cage: Petersen Graph . 7 2.1.2 (3,6)-Cage: Heawood Graph . 7 2.1.3 (3,7)-Cage: McGee Graph . 7 2.1.4 (3,8)-Cage: Tutte-Coxeter Graph . 8 2.1.5 (3,9)-Cages . 8 2.1.6 (3,10)-Cages . 9 2.1.7 (3,11)-Cage: Balaban Graph . 9 2.1.8 (3,12)-Cage: Benson Graph . 9 2.1.9 (4,5)-Cage: Robertson Graph . 9 2.1.10 (5,5)-Cages . -
18.218 Probabilistic Method in Combinatorics, Topic 3: Alterations
Lecturer: Prof. Yufei Zhao Notes by: Andrew Lin 3 Alterations Recall the naive probabilistic method: we found some lower bounds for Ramsey numbers in Section 1.1, primarily for the diagonal numbers. We did this with a basic method: color randomly, so that we color each edge red with probability p and blue with probability 1 − p. Then the probability that we don’t see any red s-cliques or blue t-cliques (with a union bound) is at most n (s ) n (t ) p 2 + (1 − p) 2 ; s t and if this is less than 1 for some p, then there exists some graph on n vertices for which there is no red Ks and blue Kt . So we union bounded the bad events there. Well, the alteration method does a little more than that - here’s a proof that mirrors that of Proposition 1.6. We again color randomly, but the idea now is to delete a vertex in every bad clique (red Ks and blue Kt ). How many edges have we deleted? We can estimate by using linearity of expectation: Theorem 3.1 For all p 2 (0; 1); n 2 N, n (s ) n (t ) R(s; t) > n − p 2 − (1 − p) 2 : s t This right hand side begins by taking the starting number of vertices and then we deleting one vertex for each clique. We’re going to explore this idea of “fixing the blemishes” a little more. 3.1 Dominating sets Definition 3.2 Given a graph G, a dominating set U is a set of vertices such that every vertex not in U has a neighbor in U. -
Properties Ii1: Girth and Circumference
Iernat. J. Math. & Math. Si. 685 Vol. 2 No. 4 (1979) 685-692 A GRAPH AND ITS COMPLEMENT WITH SPECIFIED PROPERTIES II1: GIRTH AND CIRCUMFERENCE JIN AKIYAMA AND FRANK HARARY Department of Mathematics The University of Michigan Ann Arbor, Michigan 48109 U.S.A. (Received April 5, 1979) ABSTRACT. In this series, we investigate the conditions under which both a graph G and its complement G possess certain specified properties. We now characterize all the graphs G such that both G and G have the same girth. We also determine all G such that both G and G have circumference 3 or4. KEF WORDS AND PHRASES. Graph, Complement, Girth, bmfence. 1980 MATHEMATICS SUBJECT CLASSIFICATION CODES. 05C99. iVisiting Scholar, 1978-79, from Nippon Ika University, Kawasaki, Japan. 2Vice-President, Calcutta Mathematical Society, 1978 and 1979. 686 J. AKIYAMA AND F. HARARY I. NOTATIONS AND BACKGROUND. In the first paper [2] in this series, we found all graphs G such that both G and its complement G have (a) connectivity 1, (b) line-connectivity 1, (c) mo cycles, (d) only even cycles, and other properties. Continuing this study, we determined in [3] the graphs G for which G and 6 are (a) block-graphs, (b) middle graphs, () bivariegated, and (d) n'th subdivision graphs. Now we concentrate on the two invariants concerning cycle lengths- girth and circum- ference. We will see that whenever G and G have the same girth g then g 3 or 5 only. For the circumference c we study only the cases where both G and G have c 3 or 4 Following the notation and of the G + G of two terminology [4], join 1 2 graphs is the union of G and G with the sets 1 2 complete bigraph having point V and V We will require a related ternary operation denoted G + G + G 1 2 1 2 3 on three disjoint graphs, defined as the union of the two joins G + G and 1 2 G + G Thus, this resembles the composition of the path not with just 2 3 P3 one other graph but with three graphs, one for each point; Figure 1 illustrates the "random" graph K e K + K + K Of course the quaternary operation 4 1 2 1 G + G + G + G is defined similarly.