Algorithms for Embedding Graphs in Books

Total Page:16

File Type:pdf, Size:1020Kb

Algorithms for Embedding Graphs in Books TR 85-028 ALGORITHMS FOR EMBEDDING GRAPHS IN BOOKS by Lenwood Scott Heath A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science. Chapel Hill 1985 Reader: n © 1985 Lenwood Scott Heath ALL RIGHTS RESERVED m LENWOOD SCOTT HEATH. Algorithms for Embedding Graphs in Books (Under the direction or ARNOLD L. ROSENBERG, Duke University.) ABSTRACT We investigate the problem ol embedding graphs in boob. A book is some number or half­ planes (the page• or the book), which share a common line as boundary (the qine or the book). A book embedding or a graph embeds the vertices on the spine in some order and embeds each edge in some page so that in each page no two edges intersect. The pagenumber ol a graph is the number or pages in a minimum-page embedding or the graph. The pagewidth or a book embed­ ding is the maximum cutwidth or the embedding in any page. A practical application is in the realization or a fault-tolerant array or VLSI processors. Our results are efficient algorithms for embedding certain classes or planar graphs in books or small pagenumber or small pagewidth. The first result is a linear time algorithm that embeds any planar graph in a book or seven pages. This establishes the smallest upper bound known for the pagenumber or the class or planar graphs. The algorithm uses three main ideas. The first is to level the planar graph. The second is to eztend a cycle at one level to the next level by doing micro-surgery. The third is to neat the embedding or successive levels to obtain finite pagenumber. The second result is a linear time algorithm that embeds any trivalent planar graph in a book or two pages. The algorithm edge-augments the graph to make it hamiltonian while keeping it planar. The third result is an 0( n logn) time algorithm for embedding any outerplanar graph with small pagewidth. Our algorithm embeds any ,._valent outerplanar graph in a two-page boolr. with O(dlogn) pagewidth. This result is optimal in pagewidth to within a constant factor. The significance for VLSI design is that any outerplanar graph can be implemented in small area in a fault-tolerant fashion. lv ACKNO~EDGEMENTS I dedicate this dissertation to my wife Sheila, who is my reason Cor perseverins. In the past four years, she has been my source or love, joy, support, inspiration, and meaoios. I express endless gratitude to Arnold Rosenberg for the hours he has devoted to my educ,.. tioo and Cor the belief he expressed in my abilities. Without him, this work would not have been possible. He is also responsible for pointing me in the direction which I now follow. The time and patience or the other members or my committee, Dean Brock, Tom Brylawski, Kye Hedlund, David Plaisted, and Don Stanat, are much appreciated. Don Stanat was my faith­ ful guide through the P"rils or my graduate career. I especially thank Tom Brylawski for giving me the combinatorist's view or things. My mother has always had more confidence in me than I have. Her unfailing love and belief traveled with me alons the road to this accomplishment. I thank the National Science Foundation (grant MCS-83-01213) and the Semiconductor Research Corporation (grant 41258) Cor support or this research. v TABLE OF CONTENTS 1 THE PROBLEM AND ITS MOTIVATIONS................................................................. 1 1.1 The Problem ............................................................................................................... 1 1.2 Motivations ................................................................................................................. 3 1.2.1 Multilayer VLSI Layout ........................................................................................... 3 1.2.2 Design or Fault-Tolerant Processor Arrays ............................................................... 5 1.2.3 Sorting with Parallel Stacks ..................................................................................... 6 1.3 Structure or the Di...,rtation .......... ..... .... ..... .. .. ..... .... ................ ...................... ....... .. ... 7 2 PREVIOUS RESULTS AND TOOLS ............................................................................ 8 2.1 Previous Results .......................................................................................................... 8 2.1.1 Circular Embedding ................................................................................................. 9 2.1.2 One-Page Graphs ..................................................................................................... 11 2.1.3 Two-Pase Graphs ..................................................................................................... 11 2.1.4 Planar Graphs .......................................................................................................... 12 2.2 Tools ........................................................................................................................... 12 3 EMBEDDING PLANAR GRAPHS IN SEVEN PAGES ................................................. 14 3.1 Overview ol the AJsorithm ........................................................ ............................. ..... 14 3.2 Previous Approa.:hes ................................................................................................... 19 3.2.1 The Bemhart-Kainen Conjecture ............................................................................. 19 3.2.2 Birurcators .................. ......................... ..... .... ..... ...... ..... ........................................... 25 vi 3.2.3 Separating Triangles ............. ..................... ........... ......... ........... .. ... .................. .. .. .. ... 26 3.3 Elements or the Seven-Page Algorithm ....................................................................... Z7 3.3.1 Levels ....................................................................................................................... Z7 3.3.2 D-Cycles ...................... ......... .................................... ......... ... ........................ ............ 35 3.3.3 Nesting ...... ..... ............................... ............ ........................ ........................... ....... .. ... 39 3.4 Levels Without Cycles .................................................................................... ............ 44 3.5 The Algorithm .. ....... .. .. .. ....... ....... ............. ............ ......... ...... ... .. .... ... .... .. ... .. .... ............ 46 3.5.1 The Statement ......................................................................................................... 49 3.5.2 Why Seven! ............................................................................................................ 54 3.5.3 Further Analysis .... .. .. .................... ....... ..... ........... ....... .. .. ....... .. .. ..... ......... .. .. ... .... .. ... 55 3.6 Conclusions .. ... .. ....... .. .... .............. ................ .... ....... ....... ......... .. .. .. ... .. .. .. ... .... .. ............ 56 4 EMBEDDING TRIVALENT PLANAR GRAPHS IN TWO PAGES.............................. 58 4.1 Overview or the Algorithm .......................................................................................... 58 4.2 Structure or Biconnected Planar Graphs ..................................................................... 61 4.3 The Main Theorem .. .. .. .. ... .. .. .. ..... ....... ............. .. ..... ....... .. .. .. ... .. .. .. .. .. .. ..... .. .. .. ..... .. .. ... 68 4.4 The Algorithm ........... .. .. ... .... .. ... .. ....... .. ........... ..... ...... ... .. .... ... .. .. .. ..... .. .. ... .... .. ... .. ....... 73 4.5 Oriented Face Traversal .............................................................................................. 77 4.6 Conclusions ..................................................................................................... ............ 82 5 EMBEDDING OUTERPLANAR GRAPHS IN SMALL BOOKS ....................... ............ 84 5.1 Tradeolfs ..................................................................................................................... 84 5.2 Overview or the Algorithm .... .. ..... ....... ...... ............ ...... ......... ... .. .. .. ... .. ....... .. .. .. ... .. .... ... 88 5.2.1 String Construction .................................................................................................. 92 5.2.2 Ladder Construction .. ......... ........... ....... ..... ............. ......... ..... ...... ... .... ..... .... .. ............ 05 5.3 The Algorithm ............................................................................................... :............ 101 5.4 Correctness ................................................................................................................. 105 vll 5.5 Performance ........................ .................. .. ................ .................................... .... ............ 110 5.6 Conclusion .................................................................................................................. Ill 6 CONCLUSIONS ............................................................................................................ 112 REFERENCES .................................................................................................................. 116 GLOSSARY ......................................................................................................................
Recommended publications
  • K-Outerplanar Graphs, Planar Duality, and Low Stretch Spanning Trees
    k-Outerplanar Graphs, Planar Duality, and Low Stretch Spanning Trees Yuval Emek∗ Abstract Low distortion probabilistic embedding of graphs into approximating trees is an extensively studied topic. Of particular interest is the case where the approximating trees are required to be (subgraph) spanning trees of the given graph (or multigraph), in which case, the focus is usually on the equivalent problem of finding a (single) tree with low average stretch. Among the classes of graphs that received special attention in this context are k-outerplanar graphs (for a fixed k): Chekuri, Gupta, Newman, Rabinovich, and Sinclair show that every k-outerplanar graph can be probabilistically embedded into approximating trees with constant distortion regardless of the size of the graph. The approximating trees in the technique of Chekuri et al. are not necessarily spanning trees, though. In this paper it is shown that every k-outerplanar multigraph admits a spanning tree with constant average stretch. This immediately translates to a constant bound on the distortion of probabilistically embedding k-outerplanar graphs into their spanning trees. Moreover, an efficient randomized algorithm is presented for constructing such a low average stretch spanning tree. This algorithm relies on some new insights regarding the connection between low average stretch spanning trees and planar duality. Keywords: planar graphs, outerplanarity, average stretch, planar dual. ∗Microsoft Israel R&D Center, Herzelia, Israel and School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel. E-mail: [email protected]. Supported in part by the Israel Science Foundation, grants 221/07 and 664/05. 1 Introduction The problem.
    [Show full text]
  • Planar Graph Theory We Say That a Graph Is Planar If It Can Be Drawn in the Plane Without Edges Crossing
    Planar Graph Theory We say that a graph is planar if it can be drawn in the plane without edges crossing. We use the term plane graph to refer to a planar depiction of a planar graph. e.g. K4 is a planar graph Q1: The following is also planar. Find a plane graph version of the graph. A B F E D C A Method that sometimes works for drawing the plane graph for a planar graph: 1. Find the largest cycle in the graph. 2. The remaining edges must be drawn inside/outside the cycle so that they do not cross each other. Q2: Using the method above, find a plane graph version of the graph below. A B C D E F G H non e.g. K3,3: K5 Here are three (plane graph) depictions of the same planar graph: J N M J K J N I M K K I N M I O O L O L L A face of a plane graph is a region enclosed by the edges of the graph. There is also an unbounded face, which is the outside of the graph. Q3: For each of the plane graphs we have drawn, find: V = # of vertices of the graph E = # of edges of the graph F = # of faces of the graph Q4: Do you have a conjecture for an equation relating V, E and F for any plane graph G? Q5: Can you name the 5 Platonic Solids (i.e. regular polyhedra)? (This is a geometry question.) Q6: Find the # of vertices, # of edges and # of faces for each Platonic Solid.
    [Show full text]
  • 1 Vertex Connectivity 2 Edge Connectivity 3 Biconnectivity
    1 Vertex Connectivity So far we've talked about connectivity for undirected graphs and weak and strong connec- tivity for directed graphs. For undirected graphs, we're going to now somewhat generalize the concept of connectedness in terms of network robustness. Essentially, given a graph, we may want to answer the question of how many vertices or edges must be removed in order to disconnect the graph; i.e., break it up into multiple components. Formally, for a connected graph G, a set of vertices S ⊆ V (G) is a separating set if subgraph G − S has more than one component or is only a single vertex. The set S is also called a vertex separator or a vertex cut. The connectivity of G, κ(G), is the minimum size of any S ⊆ V (G) such that G − S is disconnected or has a single vertex; such an S would be called a minimum separator. We say that G is k-connected if κ(G) ≥ k. 2 Edge Connectivity We have similar concepts for edges. For a connected graph G, a set of edges F ⊆ E(G) is a disconnecting set if G − F has more than one component. If G − F has two components, F is also called an edge cut. The edge-connectivity if G, κ0(G), is the minimum size of any F ⊆ E(G) such that G − F is disconnected; such an F would be called a minimum cut.A bond is a minimal non-empty edge cut; note that a bond is not necessarily a minimum cut.
    [Show full text]
  • CLRS B.4 Graph Theory Definitions Unit 1: DFS Informally, a Graph
    CLRS B.4 Graph Theory Definitions Unit 1: DFS informally, a graph consists of “vertices” joined together by “edges,” e.g.,: example graph G0: 1 ···················•······························· ····························· ····························· ························· ···· ···· ························· ························· ···· ···· ························· ························· ···· ···· ························· ························· ···· ···· ························· ············· ···· ···· ·············· 2•···· ···· ···· ··· •· 3 ···· ···· ···· ···· ···· ··· ···· ···· ···· ···· ···· ···· ···· ···· ···· ···· ······· ······· ······· ······· ···· ···· ···· ··· ···· ···· ···· ···· ···· ··· ···· ···· ···· ···· ···· ···· ···· ···· ···· ···· ··············· ···· ···· ··············· 4•························· ···· ···· ························· • 5 ························· ···· ···· ························· ························· ···· ···· ························· ························· ···· ···· ························· ····························· ····························· ···················•································ 6 formally a graph is a pair (V, E) where V is a finite set of elements, called vertices E is a finite set of pairs of vertices, called edges if H is a graph, we can denote its vertex & edge sets as V (H) & E(H) respectively if the pairs of E are unordered, the graph is undirected if the pairs of E are ordered the graph is directed, or a digraph two vertices joined by an edge
    [Show full text]
  • 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.
    [Show full text]
  • Efficient Multicore Algorithms for Identifying Biconnected Components
    International Journal of Networking and Computing { www.ijnc.org ISSN 2185-2839 (print) ISSN 2185-2847 (online) Volume 6, Number 1, pages 87{106, January 2016 Efficient Multicore Algorithms For Identifying Biconnected Components1 Meher Chaitanya [email protected] and Kishore Kothapalli [email protected] International Institute of Information Technology Hyderabad, Gachibowli, India 500 032. Received: July 30, 2015 Revised: October 26, 2015 Accepted: December 1, 2015 Communicated by Akihiro Fujiwara Abstract In this paper we design and implement an algorithm for finding the biconnected components of a given graph. Our algorithm is based on experimental evidence that finding the bridges of a graph is usually easier and faster in the parallel setting. We use this property to first decompose the graph into independent and maximal 2-edge-connected subgraphs. To identify the articulation points in these 2-edge connected subgraphs, we again convert this into a problem of finding the bridges on an auxiliary graph. It is interesting to note that during the conversion process, the size of the graph may increase. However, we show that this small increase in size and the run time is offset by the consideration that finding bridges is easier in a parallel setting. We implement our algorithm on an Intel i7 980X CPU running 12 threads. We show that our algorithm is on average 2.45x faster than the best known current algorithms implemented on the same platform. Finally, we extend our approach to dense graphs by applying the sparsification technique suggested by Cong and Bader in [7]. Keywords: Biconnected components, Least common ancestor, 2-edge connected components, Articulation points 1 Introduction The biconnected components of a given graph are its maximal 2-connected subgraphs.
    [Show full text]
  • 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.
    [Show full text]
  • Strongly Connected Components and Biconnected Components
    Strongly Connected Components and Biconnected Components Daniel Wisdom 27 January 2017 1 2 3 4 5 6 7 8 Figure 1: A directed graph. Credit: Crash Course Coding Companion. 1 Strongly Connected Components A strongly connected component (SCC) is a set of vertices where every vertex can reach every other vertex. In an undirected graph the SCCs are just the groups of connected vertices. We can find them using a simple DFS. This works because, in an undirected graph, u can reach v if and only if v can reach u. This is not true in a directed graph, which makes finding the SCCs harder. More on that later. 2 Kernel graph We can travel between any two nodes in the same strongly connected component. So what if we replaced each strongly connected component with a single node? This would give us what we call the kernel graph, which describes the edges between strongly connected components. Note that the kernel graph is a directed acyclic graph because any cycles would constitute an SCC, so the cycle would be compressed into a kernel. 3 1,2,5,6 4,7 8 Figure 2: Kernel graph of the above directed graph. 3 Tarjan's SCC Algorithm In a DFS traversal of the graph every SCC will be a subtree of the DFS tree. This subtree is not necessarily proper, meaning that it may be missing some branches which are their own SCCs. This means that if we can identify every node that is the root of its SCC in the DFS, we can enumerate all the SCCs.
    [Show full text]
  • Characterizations of Restricted Pairs of Planar Graphs Allowing Simultaneous Embedding with Fixed Edges
    Characterizations of Restricted Pairs of Planar Graphs Allowing Simultaneous Embedding with Fixed Edges J. Joseph Fowler1, Michael J¨unger2, Stephen Kobourov1, and Michael Schulz2 1 University of Arizona, USA {jfowler,kobourov}@cs.arizona.edu ⋆ 2 University of Cologne, Germany {mjuenger,schulz}@informatik.uni-koeln.de ⋆⋆ Abstract. A set of planar graphs share a simultaneous embedding if they can be drawn on the same vertex set V in the Euclidean plane without crossings between edges of the same graph. Fixed edges are common edges between graphs that share the same simple curve in the simultaneous drawing. Determining in polynomial time which pairs of graphs share a simultaneous embedding with fixed edges (SEFE) has been open. We give a necessary and sufficient condition for when a pair of graphs whose union is homeomorphic to K5 or K3,3 can have an SEFE. This allows us to determine which (outer)planar graphs always an SEFE with any other (outer)planar graphs. In both cases, we provide efficient al- gorithms to compute the simultaneous drawings. Finally, we provide an linear-time decision algorithm for deciding whether a pair of biconnected outerplanar graphs has an SEFE. 1 Introduction In many practical applications including the visualization of large graphs and very-large-scale integration (VLSI) of circuits on the same chip, edge crossings are undesirable. A single vertex set can be used with multiple edge sets that each correspond to different edge colors or circuit layers. While the pairwise union of all edge sets may be nonplanar, a planar drawing of each layer may be possible, as crossings between edges of distinct edge sets are permitted.
    [Show full text]
  • Minor-Closed Graph Classes with Bounded Layered Pathwidth
    Minor-Closed Graph Classes with Bounded Layered Pathwidth Vida Dujmovi´c z David Eppstein y Gwena¨elJoret x Pat Morin ∗ David R. Wood { 19th October 2018; revised 4th June 2020 Abstract We prove that a minor-closed class of graphs has bounded layered pathwidth if and only if some apex-forest is not in the class. This generalises a theorem of Robertson and Seymour, which says that a minor-closed class of graphs has bounded pathwidth if and only if some forest is not in the class. 1 Introduction Pathwidth and treewidth are graph parameters that respectively measure how similar a given graph is to a path or a tree. These parameters are of fundamental importance in structural graph theory, especially in Roberston and Seymour's graph minors series. They also have numerous applications in algorithmic graph theory. Indeed, many NP-complete problems are solvable in polynomial time on graphs of bounded treewidth [23]. Recently, Dujmovi´c,Morin, and Wood [19] introduced the notion of layered treewidth. Loosely speaking, a graph has bounded layered treewidth if it has a tree decomposition and a layering such that each bag of the tree decomposition contains a bounded number of vertices in each layer (defined formally below). This definition is interesting since several natural graph classes, such as planar graphs, that have unbounded treewidth have bounded layered treewidth. Bannister, Devanny, Dujmovi´c,Eppstein, and Wood [1] introduced layered pathwidth, which is analogous to layered treewidth where the tree decomposition is arXiv:1810.08314v2 [math.CO] 4 Jun 2020 required to be a path decomposition.
    [Show full text]
  • Coloring Clean and K4-Free Circle Graphs 401
    Coloring Clean and K4-FreeCircleGraphs Alexandr V. Kostochka and Kevin G. Milans Abstract A circle graph is the intersection graph of chords drawn in a circle. The best-known general upper bound on the chromatic number of circle graphs with clique number k is 50 · 2k. We prove a stronger bound of 2k − 1 for graphs in a simpler subclass of circle graphs, called clean graphs. Based on this result, we prove that the chromatic number of every circle graph with clique number at most 3 is at most 38. 1 Introduction Recall that the chromatic number of a graph G, denoted χ(G), is the minimum size of a partition of V(G) into independent sets. A clique is a set of pairwise adjacent vertices, and the clique number of G, denoted ω(G), is the maximum size of a clique in G. Vertices in a clique must receive distinct colors, so χ(G) ≥ ω(G) for every graph G. In general, χ(G) cannot be bounded above by any function of ω(G). Indeed, there are triangle-free graphs with arbitrarily large chromatic number [4, 18]. When graphs have additional structure, it may be possible to bound the chromatic number in terms of the clique number. A family of graphs G is χ-bounded if there is a function f such that χ(G) ≤ f (ω(G)) for each G ∈G. Some families of intersection graphs of geometric objects have been shown to be χ-bounded (see e.g. [8,11,12]). Recall that the intersection graph of a family of sets has a vertex A.V.
    [Show full text]
  • BOOK EMBEDDINGS of LAMAN GRAPHS 1. Definitions Definition 1
    BOOK EMBEDDINGS OF LAMAN GRAPHS MICHELLE DELCOURT, MEGHAN GALIARDI, AND JORDAN TIRRELL Abstract. We explore various techniques to embed planar Laman graphs in books. These graphs can be embedded as pointed pseudo-triangulations and have many interesting properties. We conjecture that all planar Laman graphs can be embedded in two pages. 1. Definitions Definition 1. A graph G with n vertices and m edges is a Laman graph if m = 2n − 3 and every subgraph induced by k vertices (where k > 1) contains at most 2k − 3 edges. Definition 2. A book embedding of a graph G is a drawing with no crossings of G on a book; every edge is mapped to a page (a half-plane), and every vertex is mapped to the spine (the boundary shared by all the half-planes). Definition 3. The book thickness of a graph G, denoted bt(G) also known as page number, is the minimum number of pages needed to book embed G. 2. Finding Book Thickness Using Henneberg Constructions Motivated by Schnyder labelings for triangulations, Huemer and Kappes proposed an analogous binary labeling for quadrangulations which decom- poses the edge set in two directed trees rooted at a sink. A weak version of this labeling is valid for Laman graphs; however, the labeling does not guarantee a decomposition into two trees. The binary labeling of Laman graphs of Huemer and Kappes is based on Henneberg constructions. The Henneberg construction provides a decomposition into two trees whereas the labeling does not. A Henneberg construction consists of two moves: I. Adding a vertex of degree two II.
    [Show full text]