Chapter 1 Planar Graphs and Testing for Planarity
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Bandwidth, Expansion, Treewidth, Separators and Universality for Bounded-Degree Graphs$
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector European Journal of Combinatorics 31 (2010) 1217–1227 Contents lists available at ScienceDirect European Journal of Combinatorics journal homepage: www.elsevier.com/locate/ejc Bandwidth, expansion, treewidth, separators and universality for bounded-degree graphsI Julia Böttcher a, Klaas P. Pruessmann b, Anusch Taraz a, Andreas Würfl a a Zentrum Mathematik, Technische Universität München, Boltzmannstraße 3, D-85747 Garching bei München, Germany b Institute for Biomedical Engineering, University and ETH Zurich, Gloriastr. 35, 8092, Zürich, Switzerland article info a b s t r a c t Article history: We establish relations between the bandwidth and the treewidth Received 3 March 2009 of bounded degree graphs G, and relate these parameters to the size Accepted 13 October 2009 of a separator of G as well as the size of an expanding subgraph of Available online 24 October 2009 G. Our results imply that if one of these parameters is sublinear in the number of vertices of G then so are all the others. This implies for example that graphs of fixed genus have sublinear bandwidth or, more generally, a corresponding result for graphs with any fixed forbidden minor. As a consequence we establish a simple criterion for universality for such classes of graphs and show for example that for each γ > 0 every n-vertex graph with minimum degree 3 C . 4 γ /n contains a copy of every bounded-degree planar graph on n vertices if n is sufficiently large. ' 2009 Elsevier Ltd. -
[Cs.CG] 1 Nov 2019 Forbidden (See [11,24,36,38] for Surveys and Reports)
An Experimental Study of a 1-planarity Testing and Embedding Algorithm ? Carla Binucci, Walter Didimo, and Fabrizio Montecchiani Universit`adegli Studi di Perugia, Italy fcarla.binucci,walter.didimo,[email protected] Abstract. The definition of 1-planar graphs naturally extends graph planarity, namely a graph is 1-planar if it can be drawn in the plane with at most one crossing per edge. Unfortunately, while testing graph planarity is solvable in linear time, deciding whether a graph is 1-planar is NP-complete, even for restricted classes of graphs. Although several polynomial-time algorithms have been described for recognizing specific subfamilies of 1-planar graphs, no implementations of general algorithms are available to date. We investigate the feasibility of a 1-planarity test- ing and embedding algorithm based on a backtracking strategy. While the experiments show that our approach can be successfully applied to graphs with up to 30 vertices, they also suggest the need of more sophis- ticated techniques to attack larger graphs. Our contribution provides initial indications that may stimulate further research on the design of practical approaches for the 1-planarity testing problem. 1 Introduction The study of sparse nonplanar graphs is receiving increasing attention in the last years. One objective of this research stream is to extend the rich set of results about planar graphs to wider families of graphs that can better model real-world problems (see, e.g., [27,28,29,30,31]). Another objective is to create readable visualizations of nonplanar networks arising in various application scenarios (see, e.g., [23,39]). -
Separator Theorems and Turán-Type Results for Planar Intersection Graphs
SEPARATOR THEOREMS AND TURAN-TYPE¶ RESULTS FOR PLANAR INTERSECTION GRAPHS JACOB FOX AND JANOS PACH Abstract. We establish several geometric extensions of the Lipton-Tarjan separator theorem for planar graphs. For instance, we show that any collection C of Jordan curves in the plane with a total of m p 2 crossings has a partition into three parts C = S [ C1 [ C2 such that jSj = O( m); maxfjC1j; jC2jg · 3 jCj; and no element of C1 has a point in common with any element of C2. These results are used to obtain various properties of intersection patterns of geometric objects in the plane. In particular, we prove that if a graph G can be obtained as the intersection graph of n convex sets in the plane and it contains no complete bipartite graph Kt;t as a subgraph, then the number of edges of G cannot exceed ctn, for a suitable constant ct. 1. Introduction Given a collection C = fγ1; : : : ; γng of compact simply connected sets in the plane, their intersection graph G = G(C) is a graph on the vertex set C, where γi and γj (i 6= j) are connected by an edge if and only if γi \ γj 6= ;. For any graph H, a graph G is called H-free if it does not have a subgraph isomorphic to H. Pach and Sharir [13] started investigating the maximum number of edges an H-free intersection graph G(C) on n vertices can have. If H is not bipartite, then the assumption that G is an intersection graph of compact convex sets in the plane does not signi¯cantly e®ect the answer. -
Sphere-Cut Decompositions and Dominating Sets in Planar Graphs
Sphere-cut Decompositions and Dominating Sets in Planar Graphs Michalis Samaris R.N. 201314 Scientific committee: Dimitrios M. Thilikos, Professor, Dep. of Mathematics, National and Kapodistrian University of Athens. Supervisor: Stavros G. Kolliopoulos, Dimitrios M. Thilikos, Associate Professor, Professor, Dep. of Informatics and Dep. of Mathematics, National and Telecommunications, National and Kapodistrian University of Athens. Kapodistrian University of Athens. white Lefteris M. Kirousis, Professor, Dep. of Mathematics, National and Kapodistrian University of Athens. Aposunjèseic sfairik¸n tom¸n kai σύνοla kuriarqÐac se epÐpeda γραφήματa Miχάλης Σάμαρης A.M. 201314 Τριμελής Epiτροπή: Δημήτρioc M. Jhlυκός, Epiblèpwn: Kajhγητής, Tm. Majhmatik¸n, E.K.P.A. Δημήτρioc M. Jhlυκός, Staύρoc G. Kolliόποuloc, Kajhγητής tou Τμήμatoc Anaπληρωτής Kajhγητής, Tm. Plhroforiκής Majhmatik¸n tou PanepisthmÐou kai Thl/ni¸n, E.K.P.A. Ajhn¸n Leutèrhc M. Kuroύσης, white Kajhγητής, Tm. Majhmatik¸n, E.K.P.A. PerÐlhyh 'Ena σημαντικό apotèlesma sth JewrÐa Γραφημάτwn apoteleÐ h apόdeixh thc eikasÐac tou Wagner από touc Neil Robertson kai Paul D. Seymour. sth σειρά ergasi¸n ‘Ελλάσσοna Γραφήματα’ apo to 1983 e¸c to 2011. H eikasÐa αυτή lèei όti sthn κλάση twn γραφημάtwn den υπάρχει άπειρη antialusÐda ¸c proc th sqèsh twn ελλασόnwn γραφημάτwn. H JewrÐa pou αναπτύχθηκε gia thn απόδειξη αυτής thc eikasÐac eÐqe kai èqei ακόμα σημαντικό antÐktupo tόσο sthn δομική όσο kai sthn algoriθμική JewrÐa Γραφημάτwn, άλλα kai se άλλα pedÐa όπως h Παραμετρική Poλυπλοκόthta. Sta πλάιsia thc απόδειξης oi suggrafeÐc eiσήγαγαν kai nèec paramètrouc πλά- touc. Se autèc ήτan h κλαδοαποσύνθεση kai to κλαδοπλάτoc ενός γραφήματoc. H παράμετρος αυτή χρησιμοποιήθηκε idiaÐtera sto σχεδιασμό algorÐjmwn kai sthn χρήση thc τεχνικής ‘διαίρει kai basÐleue’. -
Graph Planarity
Graph Planarity Alina Shaikhet Outline ▪ Definition. ▪ Motivation. ▪ Euler’s formula. ▪ Kuratowski’s theorems. ▪ Wagner’s theorem. ▪ Planarity algorithms. ▪ Properties. ▪ Crossing Number Definitions ▪ A graph is called planar if it can be drawn in a plane without any two edges intersecting. ▪ Such a drawing we call a planar embedding of the graph. ▪ A plane graph is a particular planar embedding of a planar graph. Motivation ▪ Circuit boards. Motivation ▪ Circuit boards. ▪ Connecting utilities (electricity, water, gas) to houses. Motivation ▪ Circuit boards. ▪ Connecting utilities (electricity, water, gas) to houses. Motivation ▪ Circuit boards. ▪ Connecting utilities (electricity, water, gas) to houses. ▪ Highway / Railroads / Subway design. self loops multi-edges Euler’s formula. Consider any plane embedding of a planar connected graph. Let V - be the number of vertices, E - be the number of edges and F - be the number of faces (including the single unbounded face), Then 푉 − 퐸 + 퐹 = 2. Euler formula gives the necessary condition for a graph to be planar. self loops multi-edges Euler’s formula. Consider any plane embedding of a planar connected graph. Let V - be the number of vertices, E - be the number of edges and F - be the number of faces (including the single unbounded face), Then 푉 − 퐸 + 퐹 = 2. Then 푉 − 퐸 + 퐹 = 퐶 + 1. C - is the number of connected components. 푉 − 퐸 + 퐹 = 2 Euler’s formula. 푉 = 6 퐸 = 12 퐹 = 8 푉 − 퐸 + 퐹 = 2 6 − 12 + 8 = 2 푉 − 퐸 + 퐹 = 2 Corollary 1 Let G be any plane embedding of a connected planar graph with 푉 ≥ 3 vertices. Then 1. -
Planarity Testing Revisited
Electronic Colloquium on Computational Complexity, Report No. 9 (2011) Planarity Testing Revisited Samir Datta, Gautam Prakriya Chennai Mathematical Institute India fsdatta,[email protected] Abstract. Planarity Testing is the problem of determining whether a given graph is planar while planar embedding is the corresponding construction problem. The bounded space complexity of these problems has been determined to be exactly Logspace by Allender and Mahajan [AM00] with the aid of Reingold's result [Rei08]. Unfortunately, the algorithm is quite daunting and generalizing it to say, the bounded genus case seems a tall order. In this work, we present a simple planar embedding algorithm running in logspace. We hope this algorithm will be more amenable to generalization. The algorithm is based on the fact that 3-connected planar graphs have a unique embedding, a variant of Tutte's criterion on conflict graphs of cycles and an explicit change of cycle basis. We also present a logspace algorithm to find obstacles to planarity, viz. a Kuratowski minor, if the graph is non-planar. To the best of our knowledge this is the first logspace algorithm for this problem. 1 Introduction Planarity Testing, the problem of determining whether a given graph is planar (i.e. the vertices and edges can be drawn on a plane with no edge intersections except at their end- points) is a fundamental problem in algorithmic graph theory. Along with the problem of actually obtaining a planar embedding, it is a prerequisite for many an algorithm designed to work specifically for planar graphs. Our focus is on the bounded space complexity of the planarity embedding problem be- cause we know that many graph theoretic problems like reachability [BTV09], perfect match- ing [DKR10,DKT10], and even isomorphism [DLN08,DLN+09] have efficient bounded space algorithms when provided graphs embedded on the plane. -
Linearity of Grid Minors in Treewidth with Applications Through Bidimensionality∗
Linearity of Grid Minors in Treewidth with Applications through Bidimensionality∗ Erik D. Demaine† MohammadTaghi Hajiaghayi† Abstract We prove that any H-minor-free graph, for a fixed graph H, of treewidth w has an Ω(w) × Ω(w) grid graph as a minor. Thus grid minors suffice to certify that H-minor-free graphs have large treewidth, up to constant factors. This strong relationship was previously known for the special cases of planar graphs and bounded-genus graphs, and is known not to hold for general graphs. The approach of this paper can be viewed more generally as a framework for extending combinatorial results on planar graphs to hold on H-minor-free graphs for any fixed H. Our result has many combinatorial consequences on bidimensionality theory, parameter-treewidth bounds, separator theorems, and bounded local treewidth; each of these combinatorial results has several algorithmic consequences including subexponential fixed-parameter algorithms and approximation algorithms. 1 Introduction The r × r grid graph1 is the canonical planar graph of treewidth Θ(r). In particular, an important result of Robertson, Seymour, and Thomas [38] is that every planar graph of treewidth w has an Ω(w) × Ω(w) grid graph as a minor. Thus every planar graph of large treewidth has a grid minor certifying that its treewidth is almost as large (up to constant factors). In their Graph Minor Theory, Robertson and Seymour [36] have generalized this result in some sense to any graph excluding a fixed minor: for every graph H and integer r > 0, there is an integer w > 0 such that every H-minor-free graph with treewidth at least w has an r × r grid graph as a minor. -
1 Introduction 2 Planar Graphs and Planar Separators
15-859FF: Coping with Intractability CMU, Fall 2019 Lecture #14: Planar Graphs and Planar Separators October 21, 2019 Lecturer: Jason Li Scribe: Rudy Zhou 1 Introduction In this lecture, we will consider algorithms for graph problems on a restricted class of graphs - namely, planar graphs. We will recall the definition of planar graphs, state the key property of planar graphs that our algorithms will exploit (planar separators), apply planar separators to maximum independent set, and prove the Planar Separator Theorem. 2 Planar Graphs and Planar Separators Informally speaking, a planar graph G is one that we can draw on a piece of paper such that its edges do not cross. Definition 14.1 (Planar Graph). An undirected graph G is planar if it admits a planar drawing. A planar drawing is a drawing of G in the plane such that the vertices of G are points in the plane, and the edges of G are curves connecting their respective endpoints (these curves do not have to be straight lines.) Further, we require that the edges do not cross, so they only intersect at possibly their endpoints. On planar graphs, many of our intuitions about graphs that come from drawing them on paper actually do hold. In particular, fix a planar drawing of the planar graph G. Any cycle of G divides the plane into two parts: the area outside the cycle, and the area inside the cycle. We call a chordless cycle of G a face of G. In addition, G has exactly one outer face that is unbounded on the outside. -
Graph Planarity Testing with Hierarchical Embedding Constraints
Graph Planarity Testing with Hierarchical Embedding Constraints Giuseppe Liottaa, Ignaz Rutterb, Alessandra Tappinia,∗ aDipartimento di Ingegneria, Universit`adegli Studi di Perugia, Italy bDepartment of Computer Science and Mathematics, University of Passau, Germany Abstract Hierarchical embedding constraints define a set of allowed cyclic orders for the edges incident to the vertices of a graph. These constraints are expressed in terms of FPQ-trees. FPQ-trees are a variant of PQ-trees that includes F-nodes in addition to P- and to Q-nodes. An F-node represents a permutation that is fixed, i.e., it cannot be reversed. Let G be a graph such that every vertex of G is equipped with a set of FPQ-trees encoding hierarchical embedding constraints for its incident edges. We study the problem of testing whether G admits a planar embedding such that, for each vertex v of G, the cyclic order of the edges incident to v is described by at least one of the FPQ-trees associated with v. We prove that the problem is fixed-parameter tractable for biconnected graphs, where the parameters are the treewidth of G and the number of FPQ- trees associated with every vertex of G. We also show that the problem is NP-complete if parameterized by the number of FPQ-trees only, and W[1]-hard if parameterized by the treewidth only. Besides being interesting on its own right, the study of planarity testing with hierarchical embedding constraints can be used to address other planarity testing problems. In particular, we apply our techniques to the study of NodeTrix planarity testing of clustered graphs. -
A Polynomial-Time Approximation Scheme For
A PolynomialTime Approximation Scheme for Weighted Planar Graph TSP z x y David Karger Philip Klein Michelangelo Grigni Sanjeev Arora Brown U Emory U Princeton U Andrzej Woloszyn Emory U O Abstract in n time where is any constant Then Arora and so on after Mitchell showed that a Given a planar graph on n no des with costs weights PTAS also exists for Euclidean TSP ie the subcase on its edges dene the distance b etween no des i and in which the p oints lie in and distance is measured j as the length of the shortest path b etween i and j using the Euclidean metric This PTAS also achieves Consider this as an instance of metric TSP For any O an approximation ratio in n time More our algorithm nds a salesman tour of total cost 2 O recently Arora improved the running time of his at most times optimal in time n O algorithm to O n log n using randomization We also present a quasip olynomial time algorithm The PTASs for Euclidean TSP and planargraph for the Steiner version of this problem TSP though discovered within a year of each other are quite dierent Interestingly enough Grigni et al had Intro duction conjectured the existence of a PTAS for Euclidean TSP The TSP has b een a testb ed for virtually every algorith and suggested one line of attack to design a PTAS for mic idea in the past few decades Most interesting the case of a shortestpath metric of a weighted planar subcases of the problem are NPhard so attention has graph We note later in the pap er that Euclidean TSP turned to other notions of a go o d solution -
Planar Separator Theorem
Planar Separator Theorem Aleksandar Timanov RWTH Aachen University [email protected] Abstract—Divide and conquer is a widely spread strategy for recursively, a lot of problems can be solved in an efficient solving complex problems efficiently. It is probably known to the manner. reader from some of the base algorithmic approaches for sorting In the following section (II) we are going to begin by of lists like merge sort and quick sort, which both recursively divide the list into smaller ones, sort these ones, and then merge defining some ground rules and notation which will help us the results in order to get the final sorted solution. In this paper later on. After that in section III we are going to talk about we are going to explore some of the laws by which a separator some basic theorems and lemmas and finally define and prove is defined, find efficient ways to calculate one and some of its the planar separator theorem (Theorem 4). In the IV section we applications. are going to go through an algorithm for efficient calculation of a separator in a given planar graph. In section V we are going I. INTRODUCTION to show some applications of the theorem like solving NP- In graph theory and more specifically when talking about complete problems like the maximal independent set problem planar graphs such an approach can be very useful in many and embedding data structures. We are going to finish with a situations. In the case of graphs the vertices of the graph have small conclusion in the end. -
Fast Algorithms for Hard Graph Problems: Bidimensionality, Minors, and Local Treewidth
Fast Algorithms for Hard Graph Problems: Bidimensionality, Minors, and Local Treewidth Erik D. Demaine and MohammadTaghi Hajiaghayi MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, MA 02139, USA, {edemaine,hajiagha}@mit.edu Abstract. This paper surveys the theory of bidimensional graph prob- lems. We summarize the known combinatorial and algorithmic results of this theory, the foundational Graph Minor results on which this theory is based, and the remaining open problems. 1 Introduction The newly developing theory of bidimensional graph problems, developed in a series of papers [DHT,DHN+04,DFHT,DH04a,DFHT04b,DH04b,DFHT04a, DHT04,DH05b,DH05a], provides general techniques for designing efficient fixed- parameter algorithms and approximation algorithms for NP-hard graph prob- lems in broad classes of graphs. This theory applies to graph problems that are bidimensional in the sense that (1) the solution value for the k × k grid graph (and similar graphs) grows with k, typically as Ω(k2), and (2) the solution value goes down when contracting edges and optionally when deleting edges. Examples of such problems include feedback vertex set, vertex cover, minimum maximal matching, face cover, a series of vertex-removal parameters, dominating set, edge dominating set, R-dominating set, connected dominating set, connected edge dominating set, connected R-dominating set, and unweighted TSP tour (a walk in the graph visiting all vertices). Bidimensional problems have many structural properties; for example, any graph in an appropriate minor-closed class has treewidth bounded above in terms of the problem’s solution value, typically by the square root of that value. These properties lead to efficient—often subexponential—fixed-parameter algorithms, as well as polynomial-time approximation schemes, for many minor-closed graph classes.