High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality

Total Page:16

File Type:pdf, Size:1020Kb

High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality Maciej Besta1, Armon Carigiet1, Zur Vonarburg-Shmaria1, Kacper Janda2, Lukas Gianinazzi1, Torsten Hoefler1 1Department of Computer Science, ETH Zurich 2Faculty of Computer Science, Electronics and Telecommunications, AGH-UST Abstract—We develop the first parallel graph coloring heuris- to their degrees, random (R) [26] which chooses vertices tics with strong theoretical guarantees on work and depth and uniformly at random, incidence-degree (ID) [1] which picks coloring quality. The key idea is to design a relaxation of the vertices with the largest number of uncolored neighbors first, vertex degeneracy order, a well-known graph theory concept, and to color vertices in the order dictated by this relaxation. This saturation-degree (SD) [27], where a vertex whose neighbors introduces a tunable amount of parallelism into the degeneracy use the largest number of distinct colors is chosen first, and ordering that is otherwise hard to parallelize. This simple idea smallest-degree-last (SL) [28] that removes lowest degree enables significant benefits in several key aspects of graph color- vertices, recursively colors the resulting graph, and then colors ing. For example, one of our algorithms ensures polylogarithmic the removed vertices. All these ordering heuristics, combined depth and a bound on the number of used colors that is superior to all other parallelizable schemes, while maintaining work- with Greedy, have the inherent problem of no parallelism. efficiency. In addition to provable guarantees, the developed Jones and Plassmann combined this line of work with earlier algorithms have competitive run-times for several real-world parallel schemes for deriving maximum independent sets [29], graphs, while almost always providing superior coloring quality. [30] and obtained a parallel graph coloring algorithm (JP) that Our degeneracy ordering relaxation is of separate interest for colors a vertex v once all of v’s neighbors that come later in algorithms outside the context of coloring. the provided ordering have been colored. They showed that This is the full version of a paper published at JP, combined with a random vertex ordering (JP-R), runs in ACM/IEEE Supercomputing’20 under the same title expected depth O(log n= log log n) and O(n + m) work for constant-degree graphs (n and m are #vertices and #edges I. INTRODUCTION in G, respectively). Recently, Hasenplaugh et al. [31] extended Graph coloring, more specifically vertex coloring, is a well JP with the largest-log-degree first (LLF) and smallest-log- studied problem in computer science, with many practical degree-last (SLL) orderings with better bounds on depth; these applications in domains such as sparse linear algebra computa- orderings approximate the LF and SL orderings, respectively. tions [1]–[7], conflicting task scheduling [8]–[11], networking There is also another (earlier) work [32] that – similarly to and routing [12]–[20], register allocation [21], [22], and many JP-SLL – approximates SL with the “ASL” ordering. The others [23]. A vertex coloring of a graph G is an assignment resulting coloring combines JP with ASL, we denote it as JP- of colors to vertices, such that no two neighboring vertices ASL [32]. However, it offers no bounds for work or depth. share the same color. A k-coloring is a vertex coloring of G Overall, there is no parallel algorithm with strong theoreti- which uses k distinct colors. The minimal amount of colors k cal guarantees on work and depth and quality. Whilst having for which a k-coloring can be found for G is referred to as the a reasonable theoretical run-time, JP-R may offer colorings chromatic number χ(G). An optimal coloring, also sometimes of poor quality [31], [33]. On the other hand, JP-LF and JP- referred to as the coloring problem or a χ-coloring, is the SL, which provide a better coloring quality, run in Ω(n) or problem of coloring G with χ(G) colors. Finding such and Ω(∆2) for some graphs [31]. This was addressed by the recent arXiv:2008.11321v3 [cs.DS] 11 Nov 2020 optimal coloring was shown to be NP-complete [24]. JP-LLF and JP-SLL algorithms [31] that produce colorings Nonetheless, colorings with a reasonably low number of of similarly good quality to their counterparts JP-LF and JP- colors can in practice be computed quite efficiently in the SL, and run in an expected depth that is within a logarithmic sequential setting using heuristics. One of the most important factor of JP-R. However, no guaranteed upper bounds on the is the Greedy heuristic [25], which sequentially colors vertices coloring quality (#colors), better than the trivial ∆ + 1 bound by choosing, for each selected vertex v, the smallest color not from Greedy, exist for JP-LLF, JP-SLL, or JP-ASL. already taken by v’s neighbors. This gives a guarantee for To alleviate these issues, we present the first graph coloring a coloring of G with at most ∆ + 1 colors, where ∆ is the algorithms with provably good bounds on work and depth maximum degree in G. To further improve the coloring quality and quality, simultaneously ensuring high performance and (i.e., #colors used), Greedy is in practice often used with a competitive quality in practice. The key idea is to use a certain vertex ordering heuristic, which decides the order in novel vertex ordering, the provably approximate degeneracy which Greedy colors the vertices. Example heuristics are first- ordering (ADG, contribution #1) when selecting which vertex fit (FF) [25] which uses the default order of the vertices in G, is the next to be colored. The exact degeneracy ordering is – largest-degree-first (LF) [25] which orders vertices according intuitively – an ordering obtained by iteratively removing ver- 1 tices of smallest degrees. Using the degeneracy ordering with real-world graphs, while (2) offering superior coloring quality. JP leads to the best possible Greedy coloring quality [28]. Still, Only JP-SL and JP-SLL use comparably few colors, but they computing the exact degeneracy ordering is hard to parallelize: are at least 1.5× slower. Our routines are of interest for for some graphs, it leads to Ω(n) coloring run-time [31]. To coloring both small and large graphs, for example in online tackle this, we (provably) relax the strict degeneracy order by execution scheduling and offline data analytics, respectively. assigning the same rank (in the ADG ordering) to a batch We conclude that our algorithms offer the best coloring quality of vertices that – intuitively – have similarly small degrees. at the smallest required runtime overhead. This approach also results in provably higher parallelization In a brief summary, we offer the following: because each batch of vertices can be processed in parallel. • The first parallel algorithm for deriving the (approximate) This simple idea, when applied to graph coloring, gives graph degeneracy ordering (ADG). surprisingly rich outcome. We use it to develop three novel • The first parallel graph coloring algorithm (JP-ADG), in a graph coloring algorithms that enhance two relevant lines line of heuristics based on Jones and Plassman’s scheme, of research. We first combine ADG with JP, obtaining JP- with strong bounds on work, depth, and coloring quality. ADG (contribution #2), a coloring algorithm that is paral- • The first parallel graph coloring algorithm (DEC-ADG), lelizable: vertices with the same ADG rank are colored in in a line of heuristics based on speculative coloring, with 2 parallel. It has the expected worst-case depth of O(log n + strong bounds on work, depth, and coloring quality 2 log ∆(d log n + log d log n= log log n)). Here, d is the degen- • A use case of how ADG can seamlessly enhance an existing eracy of a graph G: an upper bound on the minimal degree of state-of-the-art graph coloring scheme (DEC-ADG-ITR). every induced subgraph of G (detailed in § II-B) [28]. JP-ADG • The most extensive (so far) theoretical analysis of parallel is also work-efficient (O(n + m) work) and has good coloring graph coloring algorithms, showing advantages of our quality: it uses at most 2(1 + ")d + 1 colors1, for any " > 0. algorithms over state-of-the-art in several dimensions. Moreover, we also combine ADG with another important • Superior coloring quality offered by our algorithms over line of graph coloring algorithms that are not based on JP tuned modern schemes for many real-world graphs. but instead use speculation [32], [34]–[46]. Here, vertices We note that degeneracy ordering is used beyond graph color- are colored independently (“speculative coloring”). Potential ing [49]–[52]; thus, our ADG scheme is of separate interest. coloring conflicts (adjacent vertices assigned the same colors) are resolved by repeating coloring attempts. Combining ADG II. FUNDAMENTAL CONCEPTS with this design gives DEC-ADG (contribution #3), the first We start with background; Table I lists key symbols. Vertex scheme based on speculative coloring with provable strong coloring was already described in Section I. guarantees on all key aspects of parallel graph coloring: work O(n + m), depth O(log d log2 n) , and quality (2 + ")d. A. Graph Model and Representation Finally, we combine key design ideas in DEC-ADG with We model a graph G as a tuple (V; E); V is a set of vertices an existing recent algorithm [40] (referred to as ITR) also and E ⊆ V × V is a set of edges; jV j = n and jEj = m. We based on speculative coloring. We derive an algorithm called focus on graph coloring problems where edge directions are DEC-ADG-ITR that improves coloring quality of ITR both not relevant. Thus, G is undirected. The maximum, minimum, in theory and practice.
Recommended publications
  • A Fast and Scalable Graph Coloring Algorithm for Multi-Core and Many-Core Architectures
    A Fast and Scalable Graph Coloring Algorithm for Multi-core and Many-core Architectures Georgios Rokos1, Gerard Gorman2, and Paul H J Kelly1 1 Software Peroformance Optimisation Group, Department of Computing 2 Applied Modelling and Computation Group, Department of Earth Science and Engineering Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom, fgeorgios.rokos09,g.gorman,[email protected] Abstract. Irregular computations on unstructured data are an impor- tant class of problems for parallel programming. Graph coloring is often an important preprocessing step, e.g. as a way to perform dependency analysis for safe parallel execution. The total run time of a coloring algo- rithm adds to the overall parallel overhead of the application whereas the number of colors used determines the amount of exposed parallelism. A fast and scalable coloring algorithm using as few colors as possible is vi- tal for the overall parallel performance and scalability of many irregular applications that depend upon runtime dependency analysis. C¸ataly¨ureket al. have proposed a graph coloring algorithm which relies on speculative, local assignment of colors. In this paper we present an improved version which runs even more optimistically with less thread synchronization and reduced number of conflicts compared to C¸ataly¨urek et al.'s algorithm. We show that the new technique scales better on multi- core and many-core systems and performs up to 1.5x faster than its pre- decessor on graphs with high-degree vertices, while keeping the number of colors at the same near-optimal levels. Keywords: Graph Coloring; Greedy Coloring; First-Fit Coloring; Ir- regular Data; Parallel Graph Algorithms; Shared-Memory Parallelism; Optimistic Execution; Many-core Architectures; Intel®Xeon Phi™ 1 Introduction Many modern applications are built around algorithms which operate on irreg- ular data structures, usually in form of graphs.
    [Show full text]
  • 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).
    [Show full text]
  • Indian Journal of Science ANALYSIS International Journal for Science ISSN 2319 – 7730 EISSN 2319 – 7749
    Indian Journal of Science ANALYSIS International Journal for Science ISSN 2319 – 7730 EISSN 2319 – 7749 Graph colouring problem applied in genetic algorithm Malathi R Assistant Professor, Dept of Mathematics, Scsvmv University, Enathur, Kanchipuram, Tamil Nadu 631561, India, Email Id: [email protected] Publication History Received: 06 January 2015 Accepted: 05 February 2015 Published: 18 February 2015 Citation Malathi R. Graph colouring problem applied in genetic algorithm. Indian Journal of Science, 2015, 13(38), 37-41 ABSTRACT In this paper we present a hybrid technique that applies a genetic algorithm followed by wisdom of artificial crowds that approach to solve the graph-coloring problem. The genetic algorithm described here, utilizes more than one parent selection and mutation methods depending u p on the state of fitness of its best solution. This results in shifting the solution to the global optimum, more quickly than using a single parent selection or mutation method. The algorithm is tested against the standard DIMACS benchmark tests while, limiting the number of usable colors to the chromatic numbers. The proposed algorithm succeeded to solve the sample data set and even outperformed a recent approach in terms of the minimum number of colors needed to color some of the graphs. The Graph Coloring Problem (GCP) is also known as complete problem. Graph coloring also includes vertex coloring and edge coloring. However, mostly the term graph coloring refers to vertex coloring rather than edge coloring. Given a number of vertices, which form a connected graph, the objective is that to color each vertex so that if two vertices are connected in the graph (i.e.
    [Show full text]
  • COLORING and DEGENERACY for DETERMINING VERY LARGE and SPARSE DERIVATIVE MATRICES ASHRAFUL HUQ SUNY Bachelor of Science, Chittag
    COLORING AND DEGENERACY FOR DETERMINING VERY LARGE AND SPARSE DERIVATIVE MATRICES ASHRAFUL HUQ SUNY Bachelor of Science, Chittagong University of Engineering & Technology, 2013 A Thesis Submitted to the School of Graduate Studies of the University of Lethbridge in Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE Department of Mathematics and Computer Science University of Lethbridge LETHBRIDGE, ALBERTA, CANADA c Ashraful Huq Suny, 2016 COLORING AND DEGENERACY FOR DETERMINING VERY LARGE AND SPARSE DERIVATIVE MATRICES ASHRAFUL HUQ SUNY Date of Defence: December 19, 2016 Dr. Shahadat Hossain Supervisor Professor Ph.D. Dr. Daya Gaur Committee Member Professor Ph.D. Dr. Robert Benkoczi Committee Member Associate Professor Ph.D. Dr. Howard Cheng Chair, Thesis Examination Com- Associate Professor Ph.D. mittee Dedication To My Parents. iii Abstract Estimation of large sparse Jacobian matrix is a prerequisite for many scientific and engi- neering problems. It is known that determining the nonzero entries of a sparse matrix can be modeled as a graph coloring problem. To find out the optimal partitioning, we have proposed a new algorithm that combines existing exact and heuristic algorithms. We have introduced degeneracy and maximum k-core for sparse matrices to solve the problem in stages. Our combined approach produce better results in terms of partitioning than DSJM and for some test instances, we report optimal partitioning for the first time. iv Acknowledgments I would like to express my deep acknowledgment and profound sense of gratitude to my supervisor Dr. Shahadat Hossain, for his continuous guidance, support, cooperation, and persistent encouragement throughout the journey of my MSc program.
    [Show full text]
  • Graph Coloring Major Themes Vertex Colorings Χ(G) =
    CSC 1300 – Discrete Structures 17: Graph Coloring Major Themes • Vertex coloring • Chromac number χ(G) Graph Coloring • Map coloring • Greedy coloring algorithm • CSC 1300 – Discrete Structures Applicaons Villanova University Villanova CSC 1300 - Dr Papalaskari 1 Villanova CSC 1300 - Dr Papalaskari 2 What is the least number of colors needed for the verFces of Vertex Colorings this graph so that no two adjacent verFces have the same color? Adjacent ver,ces cannot have the same color χ(G) = Chromac number χ(G) = least number of colors needed to color the verFces of a graph so that no two adjacent verFces are assigned the same color? Source: “Discrete Mathemacs” by Chartrand & Zhang, 2011, Waveland Press. Source: “Discrete Mathemacs with Ducks” by Sara-Marie Belcastro, 2012, CRC Press, Fig 13.1. 4 5 Dr Papalaskari 1 CSC 1300 – Discrete Structures 17: Graph Coloring Map Coloring Map Coloring What is the least number of colors needed to color a map? Region è vertex Common border è edge G A D C E B F G H I Villanova CSC 1300 - Dr Papalaskari 8 Villanova CSC 1300 - Dr Papalaskari 9 Coloring the USA Four color theorem Every planar graph is 4-colorable The proof of this theorem is one of the most famous and controversial proofs in mathemacs, because it relies on a computer program. It was first presented in 1976. A more recent reformulaon can be found in this arFcle: Formal Proof – The Four Color Theorem, Georges Gonthier, NoFces of the American Mathemacal Society, December 2008. hp://www.printco.com/pages/State%20Map%20Requirements/USA-colored-12-x-8.gif
    [Show full text]
  • The Harmonious Coloring Problem Is NP-Complete for Interval and Permutation Graphsଁ Katerina Asdre, Kyriaki Ioannidou, Stavros D
    Discrete Applied Mathematics 155 (2007) 2377–2382 www.elsevier.com/locate/dam Note The harmonious coloring problem is NP-complete for interval and permutation graphsଁ Katerina Asdre, Kyriaki Ioannidou, Stavros D. Nikolopoulos Department of Computer Science, University of Ioannina, P.O. Box 1186, GR-45110 Ioannina, Greece Received 4 November 2006; received in revised form 25 May 2007; accepted 6 July 2007 Available online 14 September 2007 Abstract In this paper, we prove that the harmonious coloring problem is NP-complete for connected interval and permutation graphs. Given a simple graph G, a harmonious coloring of G is a proper vertex coloring such that each pair of colors appears together on at most one edge. The harmonious chromatic number is the least integer k for which G admits a harmonious coloring with k colors. Extending previous work on the NP-completeness of the harmonious coloring problem when restricted to the class of disconnected graphs which are simultaneously cographs and interval graphs, we prove that the problem is also NP-complete for connected interval and permutation graphs. © 2007 Elsevier B.V. All rights reserved. Keywords: Harmonious coloring; Harmonious chromatic number; Achromatic number; Interval graphs; Permutation graphs; NP-completeness 1. Introduction Many NP-complete problems on arbitrary graphs admit polynomial time algorithms when restricted to the classes of interval graphs and cographs; NP-complete problems for these two classes of graphs that become solvable in polynomial time can be found in [1,3,7,12,15,16]. However, the pair-complete coloring problem, which is NP-hard on arbitrary graphs [17], remains NP-complete when restricted to graphs that are simultaneously interval and cographs [4].Apair- complete coloring of a simple graph G is a proper vertex coloring such that each pair of colors appears together on at least one edge, while the achromatic number (G) is the largest integer k for which G admits a pair-complete coloring with k colors.
    [Show full text]
  • 3-Coloring Gnp3 in Polynomial Expected Time
    Mathematisch-Naturwissenschaftliche Fakultat¨ II Institut fur¨ Informatik Lehrstuhl fur¨ Algorithmen und Komplexit¨at Coloring sparse random k-colorable graphs in polynomial expected time Diplomarbeit eingereicht von: Julia B¨ottcher Gutachter: Dr. Amin Coja-Oghlan Dr. Mihyun Kang Berlin, den 5. Januar 2005 Erkl¨arung Hiermit erkl¨are ich, die vorliegende Arbeit selbstst¨andig verfasst und keine anderen als die angegebenen Hilfsmittel verwendet zu haben. Ich bin damit einverstanden, dass die vorliegen- de Arbeit in der Zentralbibliothek Naturwissenschaften der Humboldt-Universit¨at zu Berlin ¨offentlich ausgelegt wird. Berlin, den 5. Januar 2005 Julia B¨ottcher Zusammenfassung Nach wie vor stellt das Graphenf¨arbungsproblem eine der anspruchsvollsten algorithmischen Aufgaben innerhalb der Graphentheorie dar. So blieb bisher nicht nur die Suche nach effizienten Methoden, die dieses Problem exakt l¨osen, erfolglos. Gem¨aß eines Resultates von Feige und Kilian [22] ist auch die Existenz von signifikant besseren Approximationsalgorithmen als dem Trivialen, der jedem Knoten eine eigene Farbe zuordnet, sehr unwahrscheinlich. Diese Tatsache motiviert die Suche nach Algorithmen, die das Problem entweder nicht in allen sondern nur den meisten F¨allen entscheiden, dafur¨ aber von polynomieller Laufzeitkomplexit¨at sind, oder aber stets eine korrekte L¨osung ausgeben, eine polynomielle Laufzeit jedoch nur im Durchschnitt garantieren. Ein Algorithmus der ersterem Paradigma folgt, wurde von Alon und Kahale [2] fur¨ die fol- gende Klasse zuf¨alliger k-f¨arbbarer Graphen entwickelt: Konstruiere einen Graphen mit Gn,p,k einer Knotenmenge V der Kardinalit¨at n durch Partitionierung von V in k Mengen gleicher Gr¨oße und anschließendes Hinzufugen¨ der m¨oglichen Kanten zwischen diesen Mengen mit Wahrscheinlichkeit jeweils p.
    [Show full text]
  • Results on the Grundy Chromatic Number of Graphs Manouchehr Zaker
    Discrete Mathematics 306 (2006) 3166–3173 www.elsevier.com/locate/disc Results on the Grundy chromatic number of graphs Manouchehr Zaker Institute for Advanced Studies in Basic Sciences, 45195-1159 Zanjan, Iran Received 7 November 2003; received in revised form 22 June 2005; accepted 22 June 2005 Available online 7 August 2006 Abstract Given a graph G,byaGrundy k-coloring of G we mean any proper k-vertex coloring of G such that for each two colors i and j, i<j, every vertex of G colored by j has a neighbor with color i. The maximum k for which there exists a Grundy k-coloring is denoted by (G) and called Grundy (chromatic) number of G. We first discuss the fixed-parameter complexity of determining (G)k, for any fixed integer k and show that it is a polynomial time problem. But in general, Grundy number is an NP-complete problem. We show that it is NP-complete even for the complement of bipartite graphs and describe the Grundy number of these graphs in terms of the minimum edge dominating number of their complements. Next we obtain some additive Nordhaus–Gaddum-type inequalities concerning (G) and (Gc), for a few family of graphs. We introduce well-colored graphs, which are graphs G for which applying every greedy coloring results in a coloring of G with (G) colors. Equivalently G is well colored if (G) = (G). We prove that the recognition problem of well-colored graphs is a coNP-complete problem. © 2006 Elsevier B.V. All rights reserved. Keywords: Colorings; Chromatic number; Grundy number; First-fit colorings; NP-complete; Edge dominating sets 1.
    [Show full text]
  • Algorithmic Graph Theory Part III Perfect Graphs and Their Subclasses
    Algorithmic Graph Theory Part III Perfect Graphs and Their Subclasses Martin Milanicˇ [email protected] University of Primorska, Koper, Slovenia Dipartimento di Informatica Universita` degli Studi di Verona, March 2013 1/55 What we’ll do 1 THE BASICS. 2 PERFECT GRAPHS. 3 COGRAPHS. 4 CHORDAL GRAPHS. 5 SPLIT GRAPHS. 6 THRESHOLD GRAPHS. 7 INTERVAL GRAPHS. 2/55 THE BASICS. 2/55 Induced Subgraphs Recall: Definition Given two graphs G = (V , E) and G′ = (V ′, E ′), we say that G is an induced subgraph of G′ if V ⊆ V ′ and E = {uv ∈ E ′ : u, v ∈ V }. Equivalently: G can be obtained from G′ by deleting vertices. Notation: G < G′ 3/55 Hereditary Graph Properties Hereditary graph property (hereditary graph class) = a class of graphs closed under deletion of vertices = a class of graphs closed under taking induced subgraphs Formally: a set of graphs X such that G ∈ X and H < G ⇒ H ∈ X . 4/55 Hereditary Graph Properties Hereditary graph property (Hereditary graph class) = a class of graphs closed under deletion of vertices = a class of graphs closed under taking induced subgraphs Examples: forests complete graphs line graphs bipartite graphs planar graphs graphs of degree at most ∆ triangle-free graphs perfect graphs 5/55 Hereditary Graph Properties Why hereditary graph classes? Vertex deletions are very useful for developing algorithms for various graph optimization problems. Every hereditary graph property can be described in terms of forbidden induced subgraphs. 6/55 Hereditary Graph Properties H-free graph = a graph that does not contain H as an induced subgraph Free(H) = the class of H-free graphs Free(M) := H∈M Free(H) M-free graphT = a graph in Free(M) Proposition X hereditary ⇐⇒ X = Free(M) for some M M = {all (minimal) graphs not in X} The set M is the set of forbidden induced subgraphs for X.
    [Show full text]
  • Greedy Defining Sets of Graphs
    Greedy defining sets of graphs Manouchehr Zaker Department of Mathematical Sciences, Sharif University of Technology, P.O. Box 11365-9415, Tehran, IRAN [email protected] Abstract For a graph G and an order a on V(G), we define a greedy defining set as a subset S of V(G) with an assignment of colors to vertices in S, such that the pre-coloring can be extended to a x( G)-coloring of G by the greedy coloring of (G, a). A greedy defining set ofaX( G)-coloring C of G is a greedy defining set, which results in the coloring C (by the greedy procedure). We denote the size of a greedy defining set of C with minimum cardinality by G D N (G, a, C). In this paper we show that the problem of determining GDN(G,a,C), for an instance (G,a,C) is an NP-complete problem. 1 Introduction and preliminaries We begin with some terminology and notation which are used throughout the paper. For the other necessary definitions and notation, we refer the reader to texts such as [4]. A harmonious coloring of a graph G is a proper vertex coloring of G, such that the subgraph induced on any two different colors has at most one edge. By a hypergraph we mean an ordinary hypergraph which consists of a vertex set V and a collection of subsets of V, called (hyper)edges. In a hypergraph 1£ a block­ ing set is a subset of the vertex set which intersects every edge of 1£.
    [Show full text]
  • The Edge Grundy Numbers of Some Graphs
    b b International Journal of Mathematics and M Computer Science, 12(2017), no. 1, 13–26 CS The Edge Grundy Numbers of Some Graphs Loren Anderson1, Matthew DeVilbiss2, Sarah Holliday3, Peter Johnson4, Anna Kite5, Ryan Matzke6, Jessica McDonald4 1School of Mathematics University of Minnesota Twin Cities Minneapolis, MN 55455, USA 2Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Chicago, IL 60607-7045, USA 3Department of Mathematics Kennesaw State University Kennesaw, GA 30144, USA 4Department of Mathematics and Statistics Auburn University Auburn, AL 36849, USA 5Department of Mathematics Wayland Baptist University Plainview, TX 79072, USA 6Department of Mathematics Gettysburg College Gettysburg, PA 17325, USA email: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] (Received October 27, 2016, Accepted November 19, 2016) Key words and phrases: Grundy coloring, edge Grundy coloring, line graph, grid. AMS (MOS) Subject Classifications: 05C15. This research was supported by NSF grant no. 1262930. ISSN 1814-0432, 2017 http://ijmcs.future-in-tech.net 14 Anderson, DeVilbiss, Holliday, Johnson, Kite, Matzke, McDonald Abstract The edge Grundy numbers of graphs in a number of different classes are determined, notably for the complete and the complete bipartite graphs, as well as for the Petersen graph, the grids, and the cubes. Except for small-graph exceptions, these edge Grundy numbers turn out to equal a natural upper bound of the edge Grundy number, or to be one less than that bound. 1 Grundy colorings and Grundy numbers A Grundy coloring of a (finite simple) graph G is a proper coloring of the vertices of G with positive integers with the property that if v ∈ V (G) is colored with c > 1, then all the colors 1,...,c − 1 appear on neighbors of v.
    [Show full text]
  • Chordal Graphs MPRI 2017–2018
    Chordal graphs MPRI 2017{2018 Chordal graphs MPRI 2017{2018 Michel Habib [email protected] http://www.irif.fr/~habib Sophie Germain, septembre 2017 Chordal graphs MPRI 2017{2018 Schedule Chordal graphs Representation of chordal graphs LBFS and chordal graphs More structural insights of chordal graphs Other classical graph searches and chordal graphs Greedy colorings Chordal graphs MPRI 2017{2018 Chordal graphs Definition A graph is chordal iff it has no chordless cycle of length ≥ 4 or equivalently it has no induced cycle of length ≥ 4. I Chordal graphs are hereditary I Interval graphs are chordal Chordal graphs MPRI 2017{2018 Chordal graphs Applications I Many NP-complete problems for general graphs are polynomial for chordal graphs. I Graph theory : Treewidth (resp. pathwidth) are very important graph parameters that measure distance from a chordal graph (resp. interval graph). 1 I Perfect phylogeny 1. In fact chordal graphs were first defined in a biological modelisation pers- pective ! Chordal graphs MPRI 2017{2018 Chordal graphs G.A. Dirac, On rigid circuit graphs, Abh. Math. Sem. Univ. Hamburg, 38 (1961), pp. 71{76 Chordal graphs MPRI 2017{2018 Representation of chordal graphs About Representations I Interval graphs are chordal graphs I How can we represent chordal graphs ? I As an intersection of some family ? I This family must generalize intervals on a line Chordal graphs MPRI 2017{2018 Representation of chordal graphs Fundamental objects to play with I Maximal Cliques under inclusion I We can have exponentially many maximal cliques : A complete graph Kn in which each vertex is replaced by a pair of false twins has exactly 2n maximal cliques.
    [Show full text]