Acyclic Orientations of Graphsଁ Richard P
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I?'!'-Comparability Graphs
Discrete Mathematics 74 (1989) 173-200 173 North-Holland I?‘!‘-COMPARABILITYGRAPHS C.T. HOANG Department of Computer Science, Rutgers University, New Brunswick, NJ 08903, U.S.A. and B.A. REED Discrete Mathematics Group, Bell Communications Research, Morristown, NJ 07950, U.S.A. In 1981, Chv&tal defined the class of perfectly orderable graphs. This class of perfect graphs contains the comparability graphs. In this paper, we introduce a new class of perfectly orderable graphs, the &comparability graphs. This class generalizes comparability graphs in a natural way. We also prove a decomposition theorem which leads to a structural characteriza- tion of &comparability graphs. Using this characterization, we develop a polynomial-time recognition algorithm and polynomial-time algorithms for the clique and colouring problems for &comparability graphs. 1. Introduction A natural way to color the vertices of a graph is to put them in some order v*cv~<” l < v, and then assign colors in the following manner: (1) Consider the vertxes sequentially (in the sequence given by the order) (2) Assign to each Vi the smallest color (positive integer) not used on any neighbor vi of Vi with j c i. We shall call this the greedy coloring algorithm. An order < of a graph G is perfect if for each subgraph H of G, the greedy algorithm applied to H, gives an optimal coloring of H. An obstruction in a ordered graph (G, <) is a set of four vertices {a, b, c, d} with edges ab, bc, cd (and no other edge) and a c b, d CC. Clearly, if < is a perfect order on G, then (G, C) contains no obstruction (because the chromatic number of an obstruction is twu, but the greedy coloring algorithm uses three colors). -
CHROMATIC POLYNOMIALS of PLANE TRIANGULATIONS 1. Basic
1990, 2005 D. R. Woodall, School of Mathematical Sciences, University of Nottingham CHROMATIC POLYNOMIALS OF PLANE TRIANGULATIONS 1. Basic results. Throughout this survey, G will denote a multigraph with n vertices, m edges, c components and b blocks, and m′ will denote the smallest number of edges whose deletion from G leaves a simple graph. The corresponding numbers for Gi will be ′ denoted by ni , mi , ci , bi and mi . Let P(G, t) denote the number of different (proper vertex-) t-colourings of G. Anticipating a later result, we call P(G, t) the chromatic polynomial of G. It was introduced by G. D. Birkhoff (1912), who proved many of the following basic results. Proposition 1. (Examples.) Here Tn denotes an arbitrary tree with n vertices, Fn denotes an arbitrary forest with n vertices and c components, and Rn denotes the graph of an arbitrary triangulated polygon with n vertices: that is, a plane n-gon divided into triangles by n − 3 noncrossing chords. = n (a) P(Kn , t) t , = − n −1 (b) P(Tn , t) t(t 1) , = − − n −2 (c) P(Rn , t) t(t 1)(t 2) , = − − − + (d) P(Kn , t) t(t 1)(t 2)...(t n 1), = c − n −c (e) P(Fn , t) t (t 1) , = − n + − n − (f) P(Cn , t) (t 1) ( 1) (t 1) − = (−1)nt(t − 1)[1 + (1 − t) + (1 − t)2 + ... + (1 − t)n 2]. Proposition 2. (a) If the components of G are G1,..., Gc , then = P(G, t) P(G1, t)...P(Gc , t). = ∪ ∩ = (b) If G G1 G2 where G1 G2 Kr , then P(G1, t) P(G2, t) ¡¢¡¢¡¢¡¢¡¢¡¢¡¢¡¢¡£¡¢¡¢¡¢¡ P(G, t) = ¡ . -
Graph Orientations and Linear Extensions
GRAPH ORIENTATIONS AND LINEAR EXTENSIONS. BENJAMIN IRIARTE Abstract. Given an underlying undirected simple graph, we consider the set of all acyclic orientations of its edges. Each of these orientations induces a par- tial order on the vertices of our graph and, therefore, we can count the number of linear extensions of these posets. We want to know which choice of orien- tation maximizes the number of linear extensions of the corresponding poset, and this problem will be solved essentially for comparability graphs and odd cycles, presenting several proofs. The corresponding enumeration problem for arbitrary simple graphs will be studied, including the case of random graphs; this will culminate in 1) new bounds for the volume of the stable polytope and 2) strong concentration results for our main statistic and for the graph entropy, which hold true a:s: for random graphs. We will then argue that our problem springs up naturally in the theory of graphical arrangements and graphical zonotopes. 1. Introduction. Linear extensions of partially ordered sets have been the object of much attention and their uses and applications remain increasing. Their number is a fundamental statistic of posets, and they relate to ever-recurring problems in computer science due to their role in sorting problems. Still, many fundamental questions about linear extensions are unsolved, including the well-known 1=3-2=3 Conjecture. Efficiently enumerating linear extensions of certain posets is difficult, and the general problem has been found to be ]P-complete in Brightwell and Winkler (1991). Directed acyclic graphs, and similarly, acyclic orientations of simple undirected graphs, are closely related to posets, and their problem-modeling values in several disciplines, including the biological sciences, needs no introduction. -
Approximating the Chromatic Polynomial of a Graph
Approximating the Chromatic Polynomial Yvonne Kemper1 and Isabel Beichl1 Abstract Chromatic polynomials are important objects in graph theory and statistical physics, but as a result of computational difficulties, their study is limited to graphs that are small, highly structured, or very sparse. We have devised and implemented two algorithms that approximate the coefficients of the chromatic polynomial P (G, x), where P (G, k) is the number of proper k-colorings of a graph G for k ∈ N. Our algorithm is based on a method of Knuth that estimates the order of a search tree. We compare our results to the true chro- matic polynomial in several known cases, and compare our error with previous approximation algorithms. 1 Introduction The chromatic polynomial P (G, x) of a graph G has received much atten- tion as a result of the now-resolved four-color problem, but its relevance extends beyond combinatorics, and its domain beyond the natural num- bers. To start, the chromatic polynomial is related to the Tutte polynomial, and evaluating these polynomials at different points provides information about graph invariants and structure. In addition, P (G, x) is central in applications such as scheduling problems [26] and the q-state Potts model in statistical physics [10, 25]. The former occur in a variety of contexts, from algorithm design to factory procedures. For the latter, the relation- ship between P (G, x) and the Potts model connects statistical mechanics and graph theory, allowing researchers to study phenomena such as the behavior of ferromagnets. Unfortunately, computing P (G, x) for a general graph G is known to be #P -hard [16, 22] and deciding whether or not a graph is k-colorable is NP -hard [11]. -
Computing Tutte Polynomials
Computing Tutte Polynomials Gary Haggard1, David J. Pearce2, and Gordon Royle3 1 Bucknell University [email protected] 2 Computer Science Group, Victoria University of Wellington, [email protected] 3 School of Mathematics and Statistics, University of Western Australia [email protected] Abstract. The Tutte polynomial of a graph, also known as the partition function of the q-state Potts model, is a 2-variable polynomial graph in- variant of considerable importance in both combinatorics and statistical physics. It contains several other polynomial invariants, such as the chro- matic polynomial and flow polynomial as partial evaluations, and various numerical invariants such as the number of spanning trees as complete evaluations. However despite its ubiquity, there are no widely-available effective computational tools able to compute the Tutte polynomial of a general graph of reasonable size. In this paper we describe the implemen- tation of a program that exploits isomorphisms in the computation tree to extend the range of graphs for which it is feasible to compute their Tutte polynomials. We also consider edge-selection heuristics which give good performance in practice. We empirically demonstrate the utility of our program on random graphs. More evidence of its usefulness arises from our success in finding counterexamples to a conjecture of Welsh on the location of the real flow roots of a graph. 1 Introduction The Tutte polynomial of a graph is a 2-variable polynomial of significant im- portance in mathematics, statistical physics and biology [25]. In a strong sense it “contains” every graphical invariant that can be computed by deletion and contraction. -
The Chromatic Polynomial
Eotv¨ os¨ Lorand´ University Faculty of Science Master's Thesis The chromatic polynomial Author: Supervisor: Tam´asHubai L´aszl´oLov´aszPhD. MSc student in mathematics professor, Comp. Sci. Department [email protected] [email protected] 2009 Abstract After introducing the concept of the chromatic polynomial of a graph, we describe its basic properties and present a few examples. We continue with observing how the co- efficients and roots relate to the structure of the underlying graph, with emphasis on a theorem by Sokal bounding the complex roots based on the maximal degree. We also prove an improved version of this theorem. Finally we look at the Tutte polynomial, a generalization of the chromatic polynomial, and some of its applications. Acknowledgements I am very grateful to my supervisor, L´aszl´oLov´asz,for this interesting subject and his assistance whenever I needed. I would also like to say thanks to M´artonHorv´ath, who pointed out a number of mistakes in the first version of this thesis. Contents 0 Introduction 4 1 Preliminaries 5 1.1 Graph coloring . .5 1.2 The chromatic polynomial . .7 1.3 Deletion-contraction property . .7 1.4 Examples . .9 1.5 Constructions . 10 2 Algebraic properties 13 2.1 Coefficients . 13 2.2 Roots . 15 2.3 Substitutions . 18 3 Bounding complex roots 19 3.1 Motivation . 19 3.2 Preliminaries . 19 3.3 Proving the theorem . 20 4 Tutte's polynomial 23 4.1 Flow polynomial . 23 4.2 Reliability polynomial . 24 4.3 Statistical mechanics and the Potts model . 24 4.4 Jones polynomial of alternating knots . -
Dessin De Triangulations: Algorithmes, Combinatoire, Et Analyse
Dessin de triangulations: algorithmes, combinatoire, et analyse Eric´ Fusy Projet ALGO, INRIA Rocquencourt et LIX, Ecole´ Polytechnique – p.1/53 Motivations Display of large structures on a planar surface – p.2/53 Planar maps • A planar map is obtained by embedding a planar graph in the plane without edge crossings. • A planar map is defined up to continuous deformation Planar map The same planar map Quadrangulation Triangulation – p.3/53 Planar maps • Planar maps are combinatorial objects • They can be encoded without dealing with coordinates 2 1 Planar map Choice of labels 6 7 4 3 5 Encoding: to each vertex is associated the (cyclic) list of its neighbours in clockwise order 1: (2, 4) 2: (1, 7, 6) 3: (4, 6, 5) 4: (1, 3) 5: (3, 7) 6: (3, 2, 7) 7: (2, 5, 6) – p.4/53 Combinatorics of maps Triangulations Quadrangulations Tetravalent 3 spanning trees 2 spanning trees eulerian orientation = 2(4n−3)! = 2(3n−3)! = 2·3n(2n)! |Tn| n!(3n−2)! |Qn| n!(2n−2)! |En| n!(n+2)! ⇒ Tutte, Schaeffer, Schnyder, De Fraysseix et al... – p.5/53 A particular family of triangulations • We consider triangulations of the 4-gon (the outer face is a quadrangle) • Each 3-cycle delimits a face (irreducibility) Forbidden Irreducible – p.6/53 Transversal structures We define a transversal structure using local conditions Inner edges are colored blue or red and oriented: Inner vertex ⇒ Border vertices ⇒ Example: ⇒ – p.7/53 Link with bipolar orientations bipolar orientation = acyclic orientation with a unique minimum and a unique maximum The blue (resp. -
Computing Graph Polynomials on Graphs of Bounded Clique-Width
Computing Graph Polynomials on Graphs of Bounded Clique-Width J.A. Makowsky1, Udi Rotics2, Ilya Averbouch1, and Benny Godlin13 1 Department of Computer Science Technion{Israel Institute of Technology, Haifa, Israel [email protected] 2 School of Computer Science and Mathematics, Netanya Academic College, Netanya, Israel, [email protected] 3 IBM Research and Development Laboratory, Haifa, Israel Abstract. We discuss the complexity of computing various graph poly- nomials of graphs of fixed clique-width. We show that the chromatic polynomial, the matching polynomial and the two-variable interlace poly- nomial of a graph G of clique-width at most k with n vertices can be computed in time O(nf(k)), where f(k) ≤ 3 for the inerlace polynomial, f(k) ≤ 2k + 1 for the matching polynomial and f(k) ≤ 3 · 2k+2 for the chromatic polynomial. 1 Introduction In this paper1 we deal with the complexity of computing various graph poly- nomials of a simple graph of clique-width at most k. Our discussion focuses on the univariate characteristic polynomial P (G; λ), the matching polynomial m(G; λ), the chromatic polynomial χ(G; λ), the multivariate Tutte polyno- mial T (G; X; Y ) and the interlace polynomial q(G; XY ), cf. [Bol99], [GR01] and [ABS04b]. All these polynomials are not only of graph theoretic interest, but all of them have been motivated by or found applications to problems in chemistry, physics and biology. We give the necessary technical definitions in Section 3. Without restrictions on the graph, computing the characteristic polynomial is in P, whereas all the other polynomials are ]P hard to compute. -
Parameterized Mixed Graph Coloring
Parameterized mixed graph coloring Downloaded from: https://research.chalmers.se, 2021-09-26 10:22 UTC Citation for the original published paper (version of record): Damaschke, P. (2019) Parameterized mixed graph coloring Journal of Combinatorial Optimization, 38(2): 362-374 http://dx.doi.org/10.1007/s10878-019-00388-z N.B. When citing this work, cite the original published paper. research.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004. research.chalmers.se is administrated and maintained by Chalmers Library (article starts on next page) Journal of Combinatorial Optimization (2019) 38:362–374 https://doi.org/10.1007/s10878-019-00388-z Parameterized Mixed Graph Coloring Peter Damaschke1 Published online: 7 February 2019 © The Author(s) 2019 Abstract Coloring of mixed graphs that contain both directed arcs and undirected edges is relevant for scheduling of unit-length jobs with precedence constraints and conflicts. The classic GHRV theorem (attributed to Gallai, Hasse, Roy, and Vitaver) relates graph coloring to longest paths. It can be extended to mixed graphs. In the present paper we further extend the GHRV theorem to weighted mixed graphs. As a byproduct this yields a kernel and a parameterized algorithm (with the number of undirected edges as parameter) that is slightly faster than the brute-force algorithm. The parameter is natural since the directed version is polynomial whereas the undirected version is NP- complete. Furthermore we point out a new polynomial case where the edges form a clique. -
The Number of Orientations Having No Fixed Tournament
The number of orientations having no fixed tournament Noga Alon ∗ Raphael Yuster y Abstract Let T be a fixed tournament on k vertices. Let D(n; T ) denote the maximum number of tk 1(n) orientations of an n-vertex graph that have no copy of T . We prove that D(n; T ) = 2 − for all sufficiently (very) large n, where tk 1(n) is the maximum possible number of edges of a − graph on n vertices with no Kk, (determined by Tur´an'sTheorem). The proof is based on a directed version of Szemer´edi'sregularity lemma together with some additional ideas and tools from Extremal Graph Theory, and provides an example of a precise result proved by applying this lemma. For the two possible tournaments with three vertices we obtain separate proofs that n2=4 avoid the use of the regularity lemma and therefore show that in these cases D(n; T ) = 2b c already holds for (relatively) small values of n. 1 Introduction All graphs considered here are finite and simple. For standard terminology on undirected and directed graphs the reader is referred to [4]. Let T be some fixed tournament. An orientation of an undirected graph G = (V; E) is called T -free if it does not contain T as a subgraph. Let D(G; T ) denote the number of orientations of G that are T -free. Let D(n; T ) denote the maximum possible value of D(G; T ) where G is an n-vertex graph. In this paper we determine D(n; T ) precisely for every fixed tournament T and all sufficiently large n. -
The Connectivity of Acyclic Orientation Graphs Carla D
Discrete Mathematics 184 (1998) 281{287 Note The connectivity of acyclic orientation graphs Carla D. Savagea;∗;1, Cun-Quan Zhangb;2 a Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, USA b Department of Mathematics, West Virginia University, Morgantown, WV 26506-6310, USA Received 27 July 1995; received in revised form 5 May 1997; accepted 19 May 1997 Abstract The acyclic orientation graph, AO(G), of an undirected graph, G, is the graph whose vertices are the acyclic orientations of G and whose edges are the pairs of orientations di ering only by the reversal of one edge. Edelman (1984) has observed that it follows from results on polytopes that when G is simple, the connectivity of AO(G) is at least n − c, where n is the number of vertices and c is the number of components of G. In this paper we give a simple graph-theoretic proof of this fact. Our proof uses a result of independent interest. We establish that if H is a triangle-free graph with minimum degree at least k, and the graph obtained by contracting the edges of a matching in H is k-connected, then H is k-connected. The connectivity bound on AO(G) is tight for various graphs including Kn, Kp; q, and trees. Applications and extensions are discussed, as well as the connection with polytopes. c 1998 Elsevier Science B.V. All rights reserved 1. Introduction For an undirected graph G and an acyclic orientation D of G, an edge e of D is called dependent if reversing the direction of e creates a directed cycle in D; otherwise, e is independent [18]. -
THE CHROMATIC POLYNOMIAL 1. Introduction a Common Problem in the Study of Graph Theory Is Coloring the Vertices of a Graph So Th
THE CHROMATIC POLYNOMIAL CODY FOUTS Abstract. It is shown how to compute the Chromatic Polynomial of a sim- ple graph utilizing bond lattices and the M¨obiusInversion Theorem, which requires the establishment of a refinement ordering on the bond lattice and an exploration of the Incidence Algebra on a partially ordered set. 1. Introduction A common problem in the study of Graph Theory is coloring the vertices of a graph so that any two connected by a common edge are different colors. The vertices of the graph in Figure 1 have been colored in the desired manner. This is called a Proper Coloring of the graph. Frequently, we are concerned with determining the least number of colors with which we can achieve a proper coloring on a graph. Furthermore, we want to count the possible number of different proper colorings on a graph with a given number of colors. We can calculate each of these values by using a special function that is associated with each graph, called the Chromatic Polynomial. For simple graphs, such as the one in Figure 1, the Chromatic Polynomial can be determined by examining the structure of the graph. For other graphs, it is very difficult to compute the function in this manner. However, there is a connection between partially ordered sets and graph theory that helps to simplify the process. Utilizing subgraphs, lattices, and a special theorem called the M¨obiusInversion Theorem, we determine an algorithm for calculating the Chromatic Polynomial for any graph we choose. Figure 1. A simple graph colored so that no two vertices con- nected by an edge are the same color.