Appendix a Graph Theory Background
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A Complexity Theory for Implicit Graph Representations
A Complexity Theory for Implicit Graph Representations Maurice Chandoo Leibniz Universität Hannover, Theoretical Computer Science, Appelstr. 4, 30167 Hannover, Germany [email protected] Abstract In an implicit graph representation the vertices of a graph are assigned short labels such that adjacency of two vertices can be algorithmically determined from their labels. This allows for a space-efficient representation of graph classes that have asymptotically fewer graphs than the class of all graphs such as forests or planar graphs. A fundamental question is whether every smaller graph class has such a representation. We consider how restricting the computational complexity of such representations affects what graph classes can be represented. A formal language can be seen as implicit graph representation and therefore complexity classes such as P or NP can be understood as set of graph classes. We investigate this complexity landscape and introduce complexity classes defined in terms of first order logic that capture surprisingly many of the graph classes for which an implicit representation is known. Additionally, we provide a notion of reduction between graph classes which reveals that trees and interval graphs are ‘complete’ for certain fragments of first order logic in this setting. 1998 ACM Subject Classification G.2.2 Graph Theory, F.1.3 Complexity Measures and Classes Keywords and phrases adjacency labeling schemes, complexity of graph properties We call a graph class small if it has at most 2O(n log n) graphs on n vertices. Many important graph classes such as interval graphs are small. Using adjacency matrices or lists to represent such classes is not space-efficient since this requires n2 bits (resp. -
Abstracting Abstraction in Search II: Complexity Analysis
Proceedings of the Fifth Annual Symposium on Combinatorial Search Abstracting Abstraction in Search II: Complexity Analysis Christer Backstr¨ om¨ and Peter Jonsson Department of Computer Science, Linkoping¨ University SE-581 83 Linkoping,¨ Sweden [email protected] [email protected] Abstract In our earlier publication (Backstr¨ om¨ and Jonsson 2012) we have demonstrated the usefulness of this framework in Modelling abstraction as a function from the original state various ways. We have shown that a certain combination of space to an abstract state space is a common approach in com- our transformation properties exactly captures the DPP con- binatorial search. Sometimes this is too restricted, though, cept by Zilles and Holte (2010). A related concept is that and we have previously proposed a framework using a more flexible concept of transformations between labelled graphs. of path/plan refinement without backtracking to the abstract We also proposed a number of properties to describe and clas- level. Partial solutions to capturing this concept have been sify such transformations. This framework enabled the mod- presented in the literature, for instance, the ordered mono- elling of a number of different abstraction methods in a way tonicity criterion by Knoblock, Tenenberg, and Yang (1991), that facilitated comparative analyses. It is of particular inter- the downward refinement property (DRP) by Bacchus and est that these properties can be used to capture the concept of Yang (1994), and the simulation-based approach by Bundy refinement without backtracking between levels; how to do et al. (1996). However, how to define general conditions this has been an open question for at least twenty years. -
The Number of Dissimilar Supergraphs of a Linear Graph
Pacific Journal of Mathematics THE NUMBER OF DISSIMILAR SUPERGRAPHS OF A LINEAR GRAPH FRANK HARARY Vol. 7, No. 1 January 1957 THE NUMBER OF DISSIMILAR SUPERGRAPHS OF A LINEAR GRAPH FRANK HARARY 1. Introduction* A (p, q) graph is one with p vertices and q lines. A formula is obtained for the number of dissimilar occurrences of a given (α, β) graph H as a subgraph of all (p, q) graphs G, oc<ip, β <: q, that is, for the number of dissimilar (p, q) supergraphs of H. The enumeration methods are those of Pόlya [7]. This result is then appli- ed to obtain formulas for the number of dissimilar complete subgraphs (cliques) and cycles among all (p, q) graphs. The formula for the num- ber of rooted graphs in [2] is a special case of the number of dissimilar cliques. This note complements [3] in which the number of dissimilar {p, k) subgraphs of a given (p, q) graph is found. We conclude with a discussion of two unsolved problems. A {linear) graph G (see [5] as a general reference) consists of a finite set V of vertices together with a prescribed subset W of the col- lection of all unordered pairs of distinct vertices. The members of W are called lines and two vertices vu v% are adjacent if {vlt v2} e W, that is, if there is a line joining them. By the complement Gr of a graph G, we mean the graph whose vertex-set coincides with that of G, in which two vertices are adjacent if and only if they are not adjacent in G. -
JMM 2017 Student Poster Session Abstract Book
Abstracts for the MAA Undergraduate Poster Session Atlanta, GA January 6, 2017 Organized by Eric Ruggieri College of the Holy Cross and Chasen Smith Georgia Southern University Organized by the MAA Committee on Undergraduate Student Activities and Chapters and CUPM Subcommittee on Research by Undergraduates Dear Students, Advisors, Judges and Colleagues, If you look around today you will see over 300 posters and nearly 500 student presenters, representing a wide array of mathematical topics and ideas. These posters showcase the vibrant research being conducted as part of summer programs and during the academic year at colleges and universities from across the United States and beyond. It is so rewarding to see this session, which offers such a great opportunity for interaction between students and professional mathematicians, continue to grow. The judges you see here today are professional mathematicians from institutions around the world. They are advisors, colleagues, new Ph.D.s, and administrators. We have acknowledged many of them in this booklet; however, many judges here volunteered on site. Their support is vital to the success of the session and we thank them. We are supported financially by Tudor Investments and Two Sigma. We are also helped by the mem- bers of the Committee on Undergraduate Student Activities and Chapters (CUSAC) in some way or other. They are: Dora C. Ahmadi; Jennifer Bergner; Benjamin Galluzzo; Kristina Cole Garrett; TJ Hitchman; Cynthia Huffman; Aihua Li; Sara Louise Malec; Lisa Marano; May Mei; Stacy Ann Muir; Andy Nieder- maier; Pamela A. Richardson; Jennifer Schaefer; Peri Shereen; Eve Torrence; Violetta Vasilevska; Gerard A. -
Graph Theory, Prentice -Hall of India
Annexure 145 www.ccsenet.org/jmr Journal of Mathematics Research Vol. 3, No. 3; August 2011 Product Cordial Labeling in the Context of Tensor Product of Graphs S K Vaidya (Corresponding author) Department of Mathematics, Saurashtra University Rajkot-360 005. GUJARAT, India E-mail: [email protected] N B Vyas Atmiya Institute of Technology and Science Rajkot-360 005. GUJARAT, India E-mail: [email protected] Received: March 10, 2011 Accepted: March 28, 2011 doi:10.5539/jmr.v3n3p83 Abstract For the graph G1 and G2 the tensor product is denoted by G1(T p)G2 which is the graph with vertex set V(G1(T p)G2) = V(G1) × V(G2) and edge set E(G1(T p)G2) = {(u1, v1), (u2, v2)/u1u2E(G1) and v1v2E(G2)}. The graph Pm(T p)Pn is disconnected for ∀m, n while the graphs Cm(T p)Cn and Cm(T p)Pn are disconnected for both m and n even. We prove that these graphs are product cordial graphs. In addition to this we show that the graphs obtained by joining the connected components of respective graphs by a path of arbitrary length also admit product cordial labeling. Keywords: Cordial labeling, Porduct cordial labeling, Tensor product AMS Subject classification (2010): 05C78. 1. Introduction We begin with simple, finite and undirected graph G = (V(G), E(G)). For standard terminology and notations we follow (West, D. B, 2001). The brief summary of definitions and relevant results are given below. 1.1 Definition: If the vertices of the graph are assigned values subject to certain condition(s) then it is known as graph labeling. -
Domination in Circle Graphs
Circles graphs Dominating set Some positive results Open Problems Domination in circle graphs Nicolas Bousquet Daniel Gon¸calves George B. Mertzios Christophe Paul Ignasi Sau St´ephanThomass´e WG 2012 Domination in circle graphs Circles graphs Dominating set Some positive results Open Problems 1 Circles graphs 2 Dominating set 3 Some positive results 4 Open Problems Domination in circle graphs W [1]-hardness Under some algorithmic hypothesis, the W [1]-hard problems do not admit FPT algorithms. Circles graphs Dominating set Some positive results Open Problems Parameterized complexity FPT A problem parameterized by k is FPT (Fixed Parameter Tractable) iff it admits an algorithm which runs in time Poly(n) · f (k) for any instances of size n and of parameter k. Domination in circle graphs Circles graphs Dominating set Some positive results Open Problems Parameterized complexity FPT A problem parameterized by k is FPT (Fixed Parameter Tractable) iff it admits an algorithm which runs in time Poly(n) · f (k) for any instances of size n and of parameter k. W [1]-hardness Under some algorithmic hypothesis, the W [1]-hard problems do not admit FPT algorithms. Domination in circle graphs Circles graphs Dominating set Some positive results Open Problems Circle graphs Circle graph A circle graph is a graph which can be represented as an intersection graph of chords in a circle. 3 3 4 2 5 4 2 6 1 1 5 7 7 6 Domination in circle graphs Independent dominating sets. Connected dominating sets. Total dominating sets. All these problems are NP-complete. Circles graphs Dominating set Some positive results Open Problems Dominating set 3 4 5 2 6 1 7 Dominating set Set of chords which intersects all the chords of the graph. -
Exclusive Graph Searching Lélia Blin, Janna Burman, Nicolas Nisse
Exclusive Graph Searching Lélia Blin, Janna Burman, Nicolas Nisse To cite this version: Lélia Blin, Janna Burman, Nicolas Nisse. Exclusive Graph Searching. Algorithmica, Springer Verlag, 2017, 77 (3), pp.942-969. 10.1007/s00453-016-0124-0. hal-01266492 HAL Id: hal-01266492 https://hal.archives-ouvertes.fr/hal-01266492 Submitted on 2 Feb 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Exclusive Graph Searching∗ L´eliaBlin Sorbonne Universit´es, UPMC Univ Paris 06, CNRS, Universit´ed'Evry-Val-d'Essonne. LIP6 UMR 7606, 4 place Jussieu 75005, Paris, France [email protected] Janna Burman LRI, Universit´eParis Sud, CNRS, UMR-8623, France. [email protected] Nicolas Nisse Inria, France. Univ. Nice Sophia Antipolis, CNRS, I3S, UMR 7271, Sophia Antipolis, France. [email protected] February 2, 2016 Abstract This paper tackles the well known graph searching problem, where a team of searchers aims at capturing an intruder in a network, modeled as a graph. This problem has been mainly studied for its relationship with the pathwidth of graphs. All variants of this problem assume that any node can be simultaneously occupied by several searchers. -
Distance Labelings of Möbius Ladders
Distance Labelings of M¨obiusLadders A Major Qualifying Project Report: Submitted to the Faculty of WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Bachelor of Science by Anthony Rojas Kyle Diaz Date: March 12th; 2013 Approved: Professor Peter R. Christopher Abstract A distance-two labeling of a graph G is a function f : V (G) ! f0; 1; 2; : : : ; kg such that jf(u) − f(v)j ≥ 1 if d(u; v) = 2 and jf(u) − f(v)j ≥ 2 if d(u; v) = 1 for all u; v 2 V (G). A labeling is optimal if k is the least possible integer such that G admits a k-labeling. The λ2;1 number is the largest integer assigned to some vertex in an optimally labeled network. In this paper, we examine the λ2;1 number for M¨obiusladders, a class of graphs originally defined by Richard Guy and Frank Harary [9]. We completely determine the λ2;1 number for M¨obius ladders of even order, and for a specific class of M¨obiusladders with odd order. A general upper bound for λ2;1(G) is known [6], and for the remaining cases of M¨obiusladders we improve this bound from 18 to 7. We also provide some results for radio labelings and extensions to other labelings of these graphs. Executive Summary A graph is a pair G = (V; E), such that V (G) is the vertex set, and E(G) is the set of edges. For simple graphs (i.e., undirected, loopless, and finite), the concept of a radio labeling was first introduced in 1980 by Hale [8]. -
On Computing Longest Paths in Small Graph Classes
On Computing Longest Paths in Small Graph Classes Ryuhei Uehara∗ Yushi Uno† July 28, 2005 Abstract The longest path problem is to find a longest path in a given graph. While the graph classes in which the Hamiltonian path problem can be solved efficiently are widely investigated, few graph classes are known to be solved efficiently for the longest path problem. For a tree, a simple linear time algorithm for the longest path problem is known. We first generalize the algorithm, and show that the longest path problem can be solved efficiently for weighted trees, block graphs, and cacti. We next show that the longest path problem can be solved efficiently on some graph classes that have natural interval representations. Keywords: efficient algorithms, graph classes, longest path problem. 1 Introduction The Hamiltonian path problem is one of the most well known NP-hard problem, and there are numerous applications of the problems [17]. For such an intractable problem, there are two major approaches; approximation algorithms [20, 2, 35] and algorithms with parameterized complexity analyses [15]. In both approaches, we have to change the decision problem to the optimization problem. Therefore the longest path problem is one of the basic problems from the viewpoint of combinatorial optimization. From the practical point of view, it is also very natural approach to try to find a longest path in a given graph, even if it does not have a Hamiltonian path. However, finding a longest path seems to be more difficult than determining whether the given graph has a Hamiltonian path or not. -
Results on Domination Number and Bondage Number for Some Families of Graphs
International Journal of Pure and Applied Mathematics Volume 107 No. 3 2016, 565-577 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu AP doi: 10.12732/ijpam.v107i3.5 ijpam.eu RESULTS ON DOMINATION NUMBER AND BONDAGE NUMBER FOR SOME FAMILIES OF GRAPHS G. Hemalatha1, P. Jeyanthi2 § 1Department of Mathematics Shri Andal Alagar College of Engineering Mamandur, Kancheepuram, Tamil Nadu, INDIA 2Research Centre, Department of Mathematics Govindammal Aditanar College for Women Tiruchendur 628 215, Tamil Nadu, INDIA Abstract: Let G = (V, E) be a simple graph on the vertex set V . In a graph G, A set S ⊆ V is a dominating set of G if every vertex in VS is adjacent to some vertex in S. The domination number of a graph Gγ(G)] is the minimum size of a dominating set of vertices in G. The bondage number of a graph G[Bdγ(G)] is the cardinality of a smallest set of edges whose removal results in a graph with domination number larger than that of G. In this paper we establish domination number and the bondage number for some families of graphs. AMS Subject Classification: 05C69 Key Words: dominating set, bondage number, domination number, cocktail party graph, coxeter graph, crown graph, cubic symmetric graph, doyle graph, folkman graph, levi graph, icosahedral graph 1. Introduction The domination in graphs is one of the major areas in graph theory which attracts many researchers because it has the potential to solve many real life problems involving design and analysis of communication network, social net- Received: October 17, 2015 c 2016 Academic Publications, Ltd. -
Remembering Frank Harary
Discrete Mathematics Letters Discrete Math. Lett. 6 (2021) 1–7 www.dmlett.com DOI: 10.47443/dml.2021.s101 Editorial Remembering Frank Harary On March 11, 2005, at a special session of the 36th Southeastern International Conference on Combinatorics, Graph The- ory, and Computing held at Florida Atlantic University in Boca Raton, the famous mathematician Ralph Stanton, known for his work in combinatorics and founder of the Institute of Combinatorics and Its Applications, stated that the three mathematicians who had the greatest impact on modern graph theory have now all passed away. Stanton was referring to Frenchman Claude Berge of France, Canadian William Tutte, originally from England, and a third mathematician, an American, who had died only 66 days earlier and to whom this special session was being dedicated. Indeed, that day, March 11, 2005, would have been his 84th birthday. This third mathematician was Frank Harary. Let’s see what led Harary to be so recognized by Stanton. Frank Harary was born in New York City on March 11, 1921. He was the oldest child of Jewish immigrants from Syria and Russia. He earned a B.A. degree from Brooklyn College in 1941, spent a graduate year at Princeton University from 1943 to 1944 in theoretical physics, earned an M.A. degree from Brooklyn College in 1945, spent a year at New York University from 1945 to 1946 in applied mathematics, and then moved to the University of California at Berkeley, where he wrote his Ph.D. Thesis on The Structure of Boolean-like Rings, in 1949, under Alfred Foster. -
Discrete Mathematics Rooted Directed Path Graphs Are Leaf Powers
Discrete Mathematics 310 (2010) 897–910 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: www.elsevier.com/locate/disc Rooted directed path graphs are leaf powers Andreas Brandstädt a, Christian Hundt a, Federico Mancini b, Peter Wagner a a Institut für Informatik, Universität Rostock, D-18051, Germany b Department of Informatics, University of Bergen, N-5020 Bergen, Norway article info a b s t r a c t Article history: Leaf powers are a graph class which has been introduced to model the problem of Received 11 March 2009 reconstructing phylogenetic trees. A graph G D .V ; E/ is called k-leaf power if it admits a Received in revised form 2 September 2009 k-leaf root, i.e., a tree T with leaves V such that uv is an edge in G if and only if the distance Accepted 13 October 2009 between u and v in T is at most k. Moroever, a graph is simply called leaf power if it is a Available online 30 October 2009 k-leaf power for some k 2 N. This paper characterizes leaf powers in terms of their relation to several other known graph classes. It also addresses the problem of deciding whether a Keywords: given graph is a k-leaf power. Leaf powers Leaf roots We show that the class of leaf powers coincides with fixed tolerance NeST graphs, a Graph class inclusions well-known graph class with absolutely different motivations. After this, we provide the Strongly chordal graphs largest currently known proper subclass of leaf powers, i.e, the class of rooted directed path Fixed tolerance NeST graphs graphs.