Harary Polynomials
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On the Tree–Depth of Random Graphs Arxiv:1104.2132V2 [Math.CO] 15 Feb 2012
On the tree–depth of random graphs ∗ G. Perarnau and O.Serra November 11, 2018 Abstract The tree–depth is a parameter introduced under several names as a measure of sparsity of a graph. We compute asymptotic values of the tree–depth of random graphs. For dense graphs, p n−1, the tree–depth of a random graph G is a.a.s. td(G) = n − O(pn=p). Random graphs with p = c=n, have a.a.s. linear tree–depth when c > 1, the tree–depth is Θ(log n) when c = 1 and Θ(log log n) for c < 1. The result for c > 1 is derived from the computation of tree–width and provides a more direct proof of a conjecture by Gao on the linearity of tree–width recently proved by Lee, Lee and Oum [?]. We also show that, for c = 1, every width parameter is a.a.s. constant, and that random regular graphs have linear tree–depth. 1 Introduction An elimination tree of a graph G is a rooted tree on the set of vertices such that there are no edges in G between vertices in different branches of the tree. The natural elimination scheme provided by this tree is used in many graph algorithmic problems where two non adjacent subsets of vertices can be managed independently. One good example is the Cholesky decomposition of symmetric matrices (see [?, ?, ?, ?]). Given an elimination tree, a distributed algorithm can be designed which takes care of disjoint subsets of vertices in different parallel processors. Starting arXiv:1104.2132v2 [math.CO] 15 Feb 2012 by the furthest level from the root, it proceeds by exposing at each step the vertices at a given depth. -
THE N-DIMENSIONAL MATCHING POLYNOMIAL B. Lass
GAFA, Geom. funct. anal. Vol. 15 (2005) 453 – 475 c Birkh¨auser Verlag, Basel 2005 1016-443X/05/020453-23 DOI 10.1007/s00039-005-0512-0 GAFA Geometric And Functional Analysis THE N-DIMENSIONAL MATCHING POLYNOMIAL B. Lass To P. Cartier and A.K. Zvonkin Abstract. Heilmann et Lieb ont introduit le polynˆome de couplage µ(G, x) d’un graphe G =(V,E). Nous prolongeons leur d´efinition en munissant chaque sommet de G d’une forme lin´eaire N-dimensionnelle (ou bien d’un vecteur) et chaque arˆete d’une forme sym´etrique bilin´eaire. On attache doncatout ` r-couplage de G le produit des formes lin´eaires des sommets qui ne sont pas satur´es par le couplage, multipli´e par le produit des poids des r arˆetes du couplage, o`u le poids d’une arˆete est la valeur de sa forme ´evalu´ee sur les deux vecteurs de ses extr´emit´es. En multipliant par (−1)r et en sommant sur tous les couplages, nous obtenons notre polynˆome de couplage N-dimensionnel. Si N =1,leth´eor`eme principal de l’article de Heilmann et Lieb affirme que tous les z´eros de µ(G, x)sontr´eels. Si N =2, cependant, nous avons trouv´e des graphes exceptionnels o`u il n’y a aucun z´ero r´eel, mˆeme si chaque arˆete est munie du produit scalaire canonique. Toutefois, la th´eorie de la dualit´ed´evelopp´ee dans [La1] reste valable en N dimensions. Elle donne notamment une nouvelle interpr´etationala ` transformation de Bargmann–Segal, aux diagrammes de Feynman et aux produits de Wick. -
On Connectedness and Graph Polynomials
ON CONNECTEDNESS AND GRAPH POLYNOMIALS by Lucas Mol Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Dalhousie University Halifax, Nova Scotia April 2016 ⃝c Copyright by Lucas Mol, 2016 Table of Contents List of Tables ................................... iv List of Figures .................................. v Abstract ...................................... vii List of Abbreviations and Symbols Used .................. viii Acknowledgements ............................... x Chapter 1 Introduction .......................... 1 1.1 Background . .6 Chapter 2 All-Terminal Reliability ................... 11 2.1 An Upper Bound on the Modulus of any All-Terminal Reliability Root.................................... 13 2.1.1 All-Terminal Reliability and Simplical Complexes . 13 2.1.2 The Chip-Firing Game and Order Ideals of Monomials . 16 2.1.3 An Upper Bound on the Modulus of any All-Terminal Reliabil- ity Root . 19 2.2 All-Terminal Reliability Roots outside of the Unit Disk . 25 2.2.1 All-Terminal Reliability Roots of Larger Modulus . 25 2.2.2 Simple Graphs with All-Terminal Reliability Roots outside of the Unit Disk . 29 Chapter 3 Node Reliability ........................ 45 3.1 Monotonicity . 51 3.2 Concavity and Inflection Points . 66 3.3 Fixed Points . 74 3.4 The Roots of Node Reliability . 80 ii Chapter 4 The Connected Set Polynomial ............... 84 4.1 Complexity . 88 4.2 Roots of the Connected Set Polynomial . 99 4.2.1 Realness and Connected Set Roots . 99 4.2.2 Bounding the Connected Set Roots . 104 4.2.3 The Closure of the Collection of Connected Set Roots . 116 Chapter 5 The Subtree Polynomial ................... 130 5.1 Connected Sets and Convexity . 132 5.2 Paths and Stars . -
Testing Isomorphism in the Bounded-Degree Graph Model (Preliminary Version)
Electronic Colloquium on Computational Complexity, Revision 1 of Report No. 102 (2019) Testing Isomorphism in the Bounded-Degree Graph Model (preliminary version) Oded Goldreich∗ August 11, 2019 Abstract We consider two versions of the problem of testing graph isomorphism in the bounded-degree graph model: A version in which one graph is fixed, and a version in which the input consists of two graphs. We essentially determine the query complexity of these testing problems in the special case of n-vertex graphs with connected components of size at most poly(log n). This is done by showing that these problems are computationally equivalent (up to polylogarithmic factors) to corresponding problems regarding isomorphism between sequences (over a large alphabet). Ignoring the dependence on the proximity parameter, our main results are: 1. The query complexity of testing isomorphism to a fixed object (i.e., an n-vertex graph or an n-long sequence) is Θ(e n1=2). 2. The query complexity of testing isomorphism between two input objects is Θ(e n2=3). Testing isomorphism between two sequences is shown to be related to testing that two distributions are equivalent, and this relation yields reductions in three of the four relevant cases. Failing to reduce the problem of testing the equivalence of two distribution to the problem of testing isomorphism between two sequences, we adapt the proof of the lower bound on the complexity of the first problem to the second problem. This adaptation constitutes the main technical contribution of the current work. Determining the complexity of testing graph isomorphism (in the bounded-degree graph model), in the general case (i.e., for arbitrary bounded-degree graphs), is left open. -
Practical Verification of MSO Properties of Graphs of Bounded Clique-Width
Practical verification of MSO properties of graphs of bounded clique-width Ir`ene Durand (joint work with Bruno Courcelle) LaBRI, Universit´ede Bordeaux 20 Octobre 2010 D´ecompositions de graphes, th´eorie, algorithmes et logiques, 2010 Objectives Verify properties of graphs of bounded clique-width Properties ◮ connectedness, ◮ k-colorability, ◮ existence of cycles ◮ existence of paths ◮ bounds (cardinality, degree, . ) ◮ . How : using term automata Note that we consider finite graphs only 2/33 Graphs as relational structures For simplicity, we consider simple,loop-free undirected graphs Extensions are easy Every graph G can be identified with the relational structure (VG , edgG ) where VG is the set of vertices and edgG ⊆VG ×VG the binary symmetric relation that defines edges. v7 v6 v2 v8 v1 v3 v5 v4 VG = {v1, v2, v3, v4, v5, v6, v7, v8} edgG = {(v1, v2), (v1, v4), (v1, v5), (v1, v7), (v2, v3), (v2, v6), (v3, v4), (v4, v5), (v5, v8), (v6, v7), (v7, v8)} 3/33 Expression of graph properties First order logic (FO) : ◮ quantification on single vertices x, y . only ◮ too weak ; can only express ”local” properties ◮ k-colorability (k > 1) cannot be expressed Second order logic (SO) ◮ quantifications on relations of arbitrary arity ◮ SO can express most properties of interest in Graph Theory ◮ too complex (many problems are undecidable or do not have a polynomial solution). 4/33 Monadic second order logic (MSO) ◮ SO formulas that only use quantifications on unary relations (i.e., on sets). ◮ can express many useful graph properties like connectedness, k-colorability, planarity... Example : k-colorability Stable(X ) : ∀u, v(u ∈ X ∧ v ∈ X ⇒¬edg(u, v)) Partition(X1,..., Xm) : ∀x(x ∈ X1 ∨ . -
The Complexity of Multivariate Matching Polynomials
The Complexity of Multivariate Matching Polynomials Ilia Averbouch∗ and J.A.Makowskyy Faculty of Computer Science Israel Institute of Technology Haifa, Israel failia,[email protected] February 28, 2007 Abstract We study various versions of the univariate and multivariate matching and rook polynomials. We show that there is most general multivariate matching polynomial, which is, up the some simple substitutions and multiplication with a prefactor, the original multivariate matching polynomial introduced by C. Heilmann and E. Lieb. We follow here a line of investigation which was very successfully pursued over the years by, among others, W. Tutte, B. Bollobas and O. Riordan, and A. Sokal in studying the chromatic and the Tutte polynomial. We show here that evaluating these polynomials over the reals is ]P-hard for all points in Rk but possibly for an exception set which is semi-algebraic and of dimension strictly less than k. This result is analoguous to the characterization due to F. Jaeger, D. Vertigan and D. Welsh (1990) of the points where the Tutte polynomial is hard to evaluate. Our proof, however, builds mainly on the work by M. Dyer and C. Greenhill (2000). 1 Introduction In this paper we study generalizations of the matching and rook polynomials and their complexity. The matching polynomial was originally introduced in [5] as a multivariate polynomial. Some of its general properties, in particular the so called half-plane property, were studied recently in [2]. We follow here a line of investigation which was very successfully pursued over the years by, among others, W. Tutte [15], B. -
Every Monotone Graph Property Is Testable∗
Every Monotone Graph Property is Testable∗ Noga Alon † Asaf Shapira ‡ Abstract A graph property is called monotone if it is closed under removal of edges and vertices. Many monotone graph properties are some of the most well-studied properties in graph theory, and the abstract family of all monotone graph properties was also extensively studied. Our main result in this paper is that any monotone graph property can be tested with one-sided error, and with query complexity depending only on . This result unifies several previous results in the area of property testing, and also implies the testability of well-studied graph properties that were previously not known to be testable. At the heart of the proof is an application of a variant of Szemer´edi’s Regularity Lemma. The main ideas behind this application may be useful in characterizing all testable graph properties, and in generally studying graph property testing. As a byproduct of our techniques we also obtain additional results in graph theory and property testing, which are of independent interest. One of these results is that the query complexity of testing testable graph properties with one-sided error may be arbitrarily large. Another result, which significantly extends previous results in extremal graph-theory, is that for any monotone graph property P, any graph that is -far from satisfying P, contains a subgraph of size depending on only, which does not satisfy P. Finally, we prove the following compactness statement: If a graph G is -far from satisfying a (possibly infinite) set of monotone graph properties P, then it is at least δP ()-far from satisfying one of the properties. -
Graph Properties and Hypergraph Colourings
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Discrete Mathematics 98 (1991) 81-93 81 North-Holland Graph properties and hypergraph colourings Jason I. Brown Department of Mathematics, York University, 4700 Keele Street, Toronto, Ont., Canada M3l lP3 Derek G. Corneil Department of Computer Science, University of Toronto, Toronto, Ont., Canada MSS IA4 Received 20 December 1988 Revised 13 June 1990 Abstract Brown, J.I., D.G. Corneil, Graph properties and hypergraph colourings, Discrete Mathematics 98 (1991) 81-93. Given a graph property P, graph G and integer k 20, a P k-colouring of G is a function Jr:V(G)+ (1,. ) k} such that the subgraph induced by each colour class has property P. When P is closed under induced subgraphs, we can construct a hypergraph HG such that G is P k-colourable iff Hg is k-colourable. This correlation enables us to derive interesting new results in hypergraph chromatic theory from a ‘graphic’ approach. In particular, we build vertex critical hypergraphs that are not edge critical, construct uniquely colourable hypergraphs with few edges and find graph-to-hypergraph transformations that preserve chromatic numbers. 1. Introduction A property P is a collection of graphs (closed under isomorphism) containing K, and K, (all graphs considered here are finite); P is hereditary iff P is closed under induced subgraphs and nontrivial iff P does not contain every graph. Any graph G E P is called a P-graph. Throughout, we shall only be interested in hereditary and nontrivial properties, and P will always denote such a property. -
Isomorphism and Embedding Problems for Infinite Limits of Scale
Isomorphism and Embedding Problems for Infinite Limits of Scale-Free Graphs Robert D. Kleinberg ∗ Jon M. Kleinberg y Abstract structure of finite PA graphs; in particular, we give a The study of random graphs has traditionally been characterization of the graphs H for which the expected dominated by the closely-related models (n; m), in number of subgraph embeddings of H in an n-node PA which a graph is sampled from the uniform distributionG graph remains bounded as n goes to infinity. on graphs with n vertices and m edges, and (n; p), in n G 1 Introduction which each of the 2 edges is sampled independently with probability p. Recen tly, however, there has been For decades, the study of random graphs has been dom- considerable interest in alternate random graph models inated by the closely-related models (n; m), in which designed to more closely approximate the properties of a graph is sampled from the uniformG distribution on complex real-world networks such as the Web graph, graphs with n vertices and m edges, and (n; p), in n G the Internet, and large social networks. Two of the most which each of the 2 edges is sampled independently well-studied of these are the closely related \preferential with probability p.The first was introduced by Erd}os attachment" and \copying" models, in which vertices and R´enyi in [16], the second by Gilbert in [19]. While arrive one-by-one in sequence and attach at random in these random graphs have remained a central object \rich-get-richer" fashion to d earlier vertices. -
Distributed Testing of Graph Isomorphism in the CONGEST Model
Distributed Testing of Graph Isomorphism in the CONGEST model Reut Levi∗ Moti Medina† Abstract In this paper we study the problem of testing graph isomorphism (GI) in the CONGEST distributed model. In this setting we test whether the distributive network, GU , is isomorphic to GK which is given as an input to all the nodes in the network, or alternatively, only to a single node. We first consider the decision variant of the problem in which the algorithm should dis- tinguish the case where GU and GK are isomorphic from the case where GU and GK are not isomorphic. Specifically, if GU and GK are not isomorphic then w.h.p. at least one node should output reject and otherwise all nodes should output accept. We provide a randomized algorithm with O(n) rounds for the setting in which GK is given only to a single node. We prove that for this setting the number of rounds of any deterministic algorithm is Ω(˜ n2) rounds, where n denotes the number of nodes, which implies a separation between the randomized and the deterministic complexities of deciding GI. Our algorithm can be adapted to the semi-streaming model, where a single pass is performed and O˜(n) bits of space are used. We then consider the property testing variant of the problem, where the algorithm is only required to distinguish the case that GU and GK are isomorphic from the case that GU and GK are far from being isomorphic (according to some predetermined distance measure). We show that every (possibly randomized) algorithm, requires Ω(D) rounds, where D denotes the diameter of the network. -
Tree-Decomposition Graph Minor Theory and Algorithmic Implications
DIPLOMARBEIT Tree-Decomposition Graph Minor Theory and Algorithmic Implications Ausgeführt am Institut für Diskrete Mathematik und Geometrie der Technischen Universität Wien unter Anleitung von Univ.Prof. Dipl.-Ing. Dr.techn. Michael Drmota durch Miriam Heinz, B.Sc. Matrikelnummer: 0625661 Baumgartenstraße 53 1140 Wien Datum Unterschrift Preface The focus of this thesis is the concept of tree-decomposition. A tree-decomposition of a graph G is a representation of G in a tree-like structure. From this structure it is possible to deduce certain connectivity properties of G. Such information can be used to construct efficient algorithms to solve problems on G. Sometimes problems which are NP-hard in general are solvable in polynomial or even linear time when restricted to trees. Employing the tree-like structure of tree-decompositions these algorithms for trees can be adapted to graphs of bounded tree-width. This results in many important algorithmic applications of tree-decomposition. The concept of tree-decomposition also proves to be useful in the study of fundamental questions in graph theory. It was used extensively by Robertson and Seymour in their seminal work on Wagner’s conjecture. Their Graph Minors series of papers spans more than 500 pages and results in a proof of the graph minor theorem, settling Wagner’s conjecture in 2004. However, it is not only the proof of this deep and powerful theorem which merits mention. Also the concepts and tools developed for the proof have had a major impact on the field of graph theory. Tree-decomposition is one of these spin-offs. Therefore, we will study both its use in the context of graph minor theory and its several algorithmic implications. -
Intriguing Graph Polynomials Intriguing Graph Polynomials
ICLA-09, January 2009 Intriguing Graph Polynomials Intriguing Graph Polynomials Johann A. Makowsky Faculty of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel http://www.cs.technion.ac.il/∼janos e-mail: [email protected] ********* Joint work with I. Averbouch, M. Bl¨aser, H. Dell, B. Godlin, T. Kotek and B. Zilber Reporting also recent work by M. Freedman, L. Lov´asz, A. Schrijver and B. Szegedy Graph polynomial project: http://www.cs.technion.ac.il/∼janos/RESEARCH/gp-homepage.html 1 ICLA-09, January 2009 Intriguing Graph Polynomials Overview • Parametrized numeric graph invariants and graph polynomials • Evaluations of graph polynomials • What we find intriguing • Numeric graph invariants: Properties and guiding examples • Connection matrices • MSOL-definable graph polynomials • Finite rank of connection matrices • Applications of the Finite Rank Theorem • Complexity of evaluations of graph polynomials • Towards a dichotomy theorem 2 ICLA-09, January 2009 Intriguing Graph Polynomials References, I [CMR] B. Courcelle, J.A. Makowsky and U. Rotics: On the Fixed Parameter Complexity of Graph Enumeration Problems Definable in Monadic Second Order Logic, Discrete Applied Mathematics, 108.1-2 (2001) 23-52 [M] J.A. Makowsky: Algorithmic uses of the Feferman-Vaught theorem, Annals of Pure and Applied Logic, 126.1-3 (2004) 159-213 [M-zoo] J.A. Makowsky: From a Zoo to a Zoology: Towards a general theory of graph polynomials, Theory of Computing Systems, Special issue of CiE06, online first, October 2007 3 ICLA-09, January 2009 Intriguing Graph Polynomials References, II [MZ] J.A. Makowsky and B. Zilber, Polynomial invariants of graphs and totally categorical theories, MODNET Preprint No.