Coloring Decompositions of Complete Geometric Graphs

Total Page:16

File Type:pdf, Size:1020Kb

Coloring Decompositions of Complete Geometric Graphs COLORING DECOMPOSITIONS OF COMPLETE GEOMETRIC GRAPHS Clemens Huemer1 Dolores Lara2 Christian Rubio-Montiel3;4;5 1Departament de Matem`atiques, Universitat Polit`ecnicade Catalunya, Spain. e-mail: [email protected] 2Departamento de Computaci´on,Centro de Investigaci´ony de Estudios Avanzados del Instituto Polit´ecnicoNacional, Mexico. e-mail: [email protected] 3 Divisi´onde Matem´aticase Ingenier´ıa,FES Acatl´an,Universidad Nacional Aut´onomade M´exico.e-mail: [email protected] 4UMI LAFMIA 3175 CNRS at CINVESTAV-IPN, Mexico. 5Department of Algebra, Comenius University, Slovakia. April 24, 2019 Abstract A decomposition of a non-empty simple graph G is a pair [G; P ], such that P is a set of non-empty induced subgraphs of G, and every edge of G belongs to exactly one subgraph in P . The chromatic index χ0([G; P ]) of a decomposition [G; P ] is the smallest number k for which there exists a k-coloring of the elements of P in such a way that: for every element of P all of its edges have the same color, and if two members of P share at least one vertex, then they have different colors. A long standing conjecture of Erd}os-Faber-Lov´asz states that every decomposition [Kn;P ] 0 of the complete graph Kn satisfies χ ([Kn;P ]) ≤ n. In this paper we work with geometric graphs, and inspired by this formulation of the conjecture, we introduce the concept of chromatic index of a decomposition of the complete geometric graph. We present bounds for the chromatic index of several types of decompositions when the vertices of the graph are in This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 734922. 1 general position. We also consider the particular case when the vertices are in convex position and present bounds for the chromatic index of a few types of decompositions. Keywords: geometric graphs, coloring, geometric chromatic index. 1. Introduction In 1972, Paul Erd}os,Vance Faber and L´aszl´oLov´asz[13] made the following conjecture: CONJECTURE 1.1. Let jAij = n, 1 ≤ i ≤ n, and suppose that jAi \Ajj ≤ 1, Sn for 1 ≤ i < j ≤ n, then one can color the elements of the union i=1 Ai by n colors, so that every set contains elements of all colors. This conjecture is called the Erd}os{Faber{Lov´aszconjecture (for short EFL- conjecture) which has been formulated in terms of decompositions of graphs [3, 4]. A decomposition of a non-empty simple graph G is a pair [G; P ], such that (1) P is a set of non-empty induced subgraphs of G, and (2) every edge of G belongs to exactly one subgraph in P . We can think of P as being a partition of the edges of G. A k-P -coloring (or a k-P -proper coloring) of a decomposition [G; P ] is a function that assigns to each edge of G a color from a set of k colors, so that (1) for every H 2 P all of its edges have the same color, and (2) for any two H1;H2 2 P , if the two graphs have at least one vertex in common, then 0 E(H1) and E(H2) have different colors. The chromatic index χ ([G; P ]) of a decomposition [G; P ] is the smallest number k for which there exists a k-P - coloring of [G; P ]. The authors of [3] and [4] give an equivalent formulation of the well-known EFL-conjecture in terms of the chromatic index of decompositions of the com- plete graph: CONJECTURE 1.2. Every decomposition [Kn;P ] of the complete graph Kn satisfies 0 χ ([Kn;P ]) ≤ n: Note that for every decomposition [Kn;P ] of the complete graph Kn, since P is a set of non-empty induced subgraphs of Kn, the set P consists of complete subgraphs of Kn. To avoid repetition, we do not reiterate this in what follows, but we ask the reader to keep it in mind throughout the paper. In this paper, inspired by Conjecture 1.2, we study a problem about de- compositions of complete geometric graphs. A geometric graph G1 is a drawing in the plane of a graph G, such that its vertices are points in general position (no three of them are collinear), and its edges are straight-line segments. Two 1To avoid any possible confusion, geometric graphs will be denoted by Sans Serif Math letters. 2 geometric graphs have nonempty intersection if either (1) they have a common vertex, or (2) there exists a pair of edges, one from each graph, that crosses. A complete geometric graph of order n, denoted Kn, is a graph in wich each vertex is adjacent to every other vertex. A decomposition [G; P ] of a graph G naturally induces a decomposition [G;P ] of any geometric graph G.A k-P -coloring (or a k-P -proper coloring) of a decomposition [G;P ] maps to the edges of G a color from a set with k colors, so that (1) for every H 2 P all of its edges have the same color, and (2) for any two H1; H2 2 P , if the two graphs have nonempty intersection, then E(H1) and E(H2) have different colors. The smallest positive integer k for which there is a k-P -coloring of [G;P ] is the chromatic index of a decomposition [G;P ] and it is denoted by χ0([G;P ]). In this paper we give lower and upper bounds for the chromatic index of several families of decompositions (in complete subgraphs) of complete geomet- ric graphs. Furthermore, we state a conjecture on an upper bound on χ0([G;P ]) for any partition P . The paper is organized as follows. In Section 2 we consider complete geometric graphs whose set of vertices are points in general position, in Section 3 we consider the case in which the vertices of the complete geometric graph are in convex position (they are the vertices of a convex polygon), finally, in Section 4 we state a few conjectures. 1.1 Notation, Terminology and Definitions Throughout this paper we assume that all sets of points in the plane are in general position. Note that every set of n points in the plane induces a complete geometric graph. Let S be a set of n points in the plane and let Kn be the complete geometric graph induced by S. For brevity, we refer to the points in S as vertices of Kn, and to the straight-line segments connecting two points in S as the edges of Kn. In the remainder of this subsection we present some definitions related with designs. DEFINITION 1.1. Given integers n and κ, a 2-(n; κ)-design D is a pair (P; B), where P is a set of n elements, and B is a collection of κ-subsets of P with the property that each subset of P of size 2 is a subset of exactly one member of B. The members of P are called points and the members of B are called blocks. DEFINITION 1.2. D = (Zn; B) is a cyclic design if B is fixed by the auto- morphism i 7! i + 1. When it is clear from the context, we refer to a 2-(n; κ)-design simply as 2- design. 2-designs are commonly known in the literature as BIBD, standing for balanced incomplete block design. We can think about any 2-design D as a decomposition of the complete graph. That is, D is a particular case of a decomposition of the complete graph, in which, in the natural way, every block is an element of the partition, see [16]. κn It was proven in [9] that any 2-design has chromatic index at most κ−1 , and that, every cyclic 2-design satisfies the EFL conjecture. However, in general, 3 Figure 1: The unique Steiner triple system ST S(9) as a decomposition of K9. the EFL conjecture is open for 2-designs, even for κ = 3. A 2-(n; 3)-design is called Steiner triple system, and it is denoted by ST S(n). It is well-known that ST S(n) exists if and only if n is congruent to 1 or 3 modulo 6 (see [18]). It is also well-known that a cyclic ST S(n) exists if and only if n 6= 9 and n is congruent to 1 or 3 modulo 6 (see [11, 17]). Figure 1 shows the unique ST S(9), see [10]. There is another special type of well-known 2-design: A projective plane is a 2-(q2 + q + 1; q + 1)-design having the following properties: 1. Given any two distinct blocks there is exactly one point incident with both of them. 2. There are four points so that no line is incident with more than two of them. The number q is called the order of the projective plane. A projective plane of order q is denoted by Πq. It is known [15] that there exists at least one projective plane of order q for any prime power q. 2. Points in General Position Let D be a 2-(n; 2)-design. D induces the decomposition [Kn;E(Kn)] of Kn. 0 Therefore, Vizing's theorem verifies the EFL conjecture, since χ ([Kn;E(Kn)]) 0 0 is the usual chromatic index χ (Kn) of the complete graph, and χ (Kn) ≤ n.
Recommended publications
  • Representing Graphs by Polygons with Edge Contacts in 3D
    Representing Graphs by Polygons with Side Contacts in 3D∗ Elena Arseneva1, Linda Kleist2, Boris Klemz3, Maarten Löffler4, André Schulz5, Birgit Vogtenhuber6, and Alexander Wolff7 1 Saint Petersburg State University, Russia [email protected] 2 Technische Universität Braunschweig, Germany [email protected] 3 Freie Universität Berlin, Germany [email protected] 4 Utrecht University, the Netherlands [email protected] 5 FernUniversität in Hagen, Germany [email protected] 6 Technische Universität Graz, Austria [email protected] 7 Universität Würzburg, Germany orcid.org/0000-0001-5872-718X Abstract A graph has a side-contact representation with polygons if its vertices can be represented by interior-disjoint polygons such that two polygons share a common side if and only if the cor- responding vertices are adjacent. In this work we study representations in 3D. We show that every graph has a side-contact representation with polygons in 3D, while this is not the case if we additionally require that the polygons are convex: we show that every supergraph of K5 and every nonplanar 3-tree does not admit a representation with convex polygons. On the other hand, K4,4 admits such a representation, and so does every graph obtained from a complete graph by subdividing each edge once. Finally, we construct an unbounded family of graphs with average vertex degree 12 − o(1) that admit side-contact representations with convex polygons in 3D. Hence, such graphs can be considerably denser than planar graphs. 1 Introduction A graph has a contact representation if its vertices can be represented by interior-disjoint geometric objects1 such that two objects touch exactly if the corresponding vertices are adjacent.
    [Show full text]
  • Analytical Framework for Recurrence-Network Analysis of Time Series
    Analytical framework for recurrence-network analysis of time series Jonathan F. Donges,1, 2, ∗ Jobst Heitzig,1 Reik V. Donner,1 and J¨urgenKurths1, 2, 3 1Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412 Potsdam, Germany 2Department of Physics, Humboldt University Berlin, Newtonstr. 15, 12489 Berlin, Germany 3Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3FX, United Kingdom (Dated: August 13, 2018) Recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems. While there are already many successful applications ranging from medicine to paleoclima- tology, a solid theoretical foundation of the method has still been missing so far. Here, we interpret an "-recurrence network as a discrete subnetwork of a \continuous" graph with uncountably many vertices and edges corresponding to the system's attractor. This step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ". In particular, we introduce local measures such as the "-clustering coefficient, mesoscopic measures such as "-motif density, path-based measures such as "-betweennesses, and global measures such as "-efficiency. This new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of " via a percolation threshold, and it shows that estimation can be improved by so-called node splitting invariant versions of the measures. We finally illustrate the framework for a number of archetypical chaotic attractors such as those of the Bernoulli and logistic maps, periodic and two-dimensional quasi-periodic motions, and for hyperballs and hypercubes, by deriving analytical expressions for the novel measures and com- paring them with data from numerical experiments.
    [Show full text]
  • Geometric Biplane Graphs I: Maximal Graphs ∗
    Geometric Biplane Graphs I: Maximal Graphs ∗ Alfredo Garc´ıa1 Ferran Hurtado2 Matias Korman3;4 In^esMatos5 Maria Saumell6 Rodrigo I. Silveira5;2 Javier Tejel1 Csaba D. T´oth7 Abstract We study biplane graphs drawn on a finite planar point set S in general position. This is the family of geometric graphs whose vertex set is S and can be decomposed into two plane graphs. We show that two maximal biplane graphs|in the sense that no edge can be added while staying biplane|may differ in the number of edges, and we provide an efficient algorithm for adding edges to a biplane graph to make it maximal. We also study extremal properties of maximal biplane graphs such as the maximum number of edges and the largest maximum connectivity over n-element point sets. 1 Introduction In a geometric graph G = (V; E), the vertices are distinct points in the plane in general position, and the edges are straight line segments between pairs of vertices. A plane graph is a geometric graph in which no two edges cross. Every (abstract) graph has a realization as a geometric graph (by simply mapping the vertices into distinct points in the plane, no three of which are collinear), and every planar graph can be realized as a plane graph by F´ary'stheorem [13]. The number of n-vertex labeled planar graphs is at least 27:22n n! [15]. · However, there are only 2O(n) plane graphs on any given set of n points in the plane [2, 22]. We consider a generalization of plane graphs.
    [Show full text]
  • Penrose M. Random Geometric Graphs (OUP, 2003)(ISBN 0198506260)(345S) Mac .Pdf
    OXFORD STUDIES IN PROBABILITYManaging Editor L. C. G. ROGERS Editorial Board P. BAXENDALE P. GREENWOOD F. P. KELLY J.-F. LE GALL E. PARDOUX D. WILLIAMS OXFORD STUDIES IN PROBABILITY 1. F. B. Knight: Foundations of the prediction process 2. A. D. Barbour, L. Holst, and S. Janson: Poisson approximation 3. J. F. C. Kingman: Poisson processes 4. V. V. Petrov: Limit theorems of probability theory 5. M. Penrose: Random geometric graphs Random Geometric Graphs MATHEW PENROSE University of Bath Great Clarendon Street, Oxford OX26DP Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Bangkok Buenos Aires Cape Town Chennai Dar es Salaam Delhi Hong Kong Istanbul Kaarachi Kolkata Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi São Paulo Shanghai Taipei Tokyo Toronto Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York © Mathew Penrose, 2003 The moral rights of the author have been asserted Database right Oxford University Press (maker) First published 2003 Reprinted 2004 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organisation. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above.
    [Show full text]
  • COLORING GRAPHS USING TOPOLOGY 11 Realized by Tutte Who Called an Example of a Disc G for Which the Boundary Has Chromatic Number 4 a Chromatic Obstacle
    COLORING GRAPHS USING TOPOLOGY OLIVER KNILL Abstract. Higher dimensional graphs can be used to color 2- dimensional geometric graphs G. If G the boundary of a three dimensional graph H for example, we can refine the interior until it is colorable with 4 colors. The later goal is achieved if all inte- rior edge degrees are even. Using a refinement process which cuts the interior along surfaces S we can adapt the degrees along the boundary S. More efficient is a self-cobordism of G with itself with a host graph discretizing the product of G with an interval. It fol- lows from the fact that Euler curvature is zero everywhere for three dimensional geometric graphs, that the odd degree edge set O is a cycle and so a boundary if H is simply connected. A reduction to minimal coloring would imply the four color theorem. The method is expected to give a reason \why 4 colors suffice” and suggests that every two dimensional geometric graph of arbitrary degree and ori- entation can be colored by 5 colors: since the projective plane can not be a boundary of a 3-dimensional graph and because for higher genus surfaces, the interior H is not simply connected, we need in general to embed a surface into a 4-dimensional simply connected graph in order to color it. This explains the appearance of the chromatic number 5 for higher degree or non-orientable situations, a number we believe to be the upper limit. For every surface type, we construct examples with chromatic number 3; 4 or 5, where the construction of surfaces with chromatic number 5 is based on a method of Fisk.
    [Show full text]
  • Geometric Intersection Patterns and the Theory of Topological Graphs
    Geometric Intersection Patterns and the Theory of Topological Graphs J´anosPach∗ Abstract. The intersection graph of a set system S is a graph on the vertex set S, in which two vertices are connected by an edge if and only if the corresponding sets have nonempty intersection. It was shown by Tietze (1905) that every finite graph is the intersection graph of 3-dimensional convex polytopes. The analogous statement is false in any fixed dimension if the polytopes are allowed to have only a bounded number of faces or are replaced by simple geometric objects that can be described in terms of a bounded number of real parameters. Intersection graphs of various classes of geometric objects, even in the plane, have interesting structural and extremal properties. We survey problems and results on geometric intersection graphs and, more gener- ally, intersection patterns. Many of the questions discussed were originally raised by Berge, Erd}os,Gr¨unbaum, Hadwiger, Tur´an,and others in the context of classical topol- ogy, graph theory, and combinatorics (related, e.g., to Helly's theorem, Ramsey theory, perfect graphs). The rapid development of computational geometry and graph drawing algorithms in the last couple of decades gave further impetus to research in this field. A topological graph is a graph drawn in the plane so that its vertices are represented by points and its edges by possibly intersecting simple continuous curves connecting the corresponding point pairs. We give applications of the results concerning intersection patterns in the theory of topological graphs. Mathematics Subject Classification (2010). Primary 05C35; Secondary 05C62, 52C10.
    [Show full text]
  • Compatible Geometric Matchings$
    Compatible Geometric MatchingsI Oswin Aichholzera,1, Sergey Beregb, Adrian Dumitrescuc,2, Alfredo Garc´ıad,3, Clemens Huemere,4, Ferran Hurtadoe,4, Mikio Kanof, Alberto M´arquezg, David Rappaporth,5, Shakhar Smorodinskyi, Diane Souvainej, Jorge Urrutiak,6, David R. Woodl,∗,4,7 aInstitute for Software Technology, Graz University of Technology, Austria bDepartment of Computer Science, University of Texas at Dallas, U.S.A. cDepartment of Computer Science, University of Wisconsin-Milwaukee, U.S.A. dDepartamento de M´etodos Estad´ısticos, Universidad de Zaragoza, Spain eDepartament de Matem`atica Aplicada II, Universitat Polit`ecnica de Catalunya, Spain fDepartment of Computer and Information Sciences, Ibaraki University, Japan gDepartamento de Matem´atica Aplicada I, Universidad de Sevilla, Spain hSchool of Computing, Queen's University, Canada iDepartment of Mathematics, Ben-Gurion University, Israel jDepartment of Computer Science, Tufts University, U.S.A. kInstituto de Matem´aticas, Universidad Nacional Aut´onomade M´exico, M´exico lDepartment of Mathematics and Statistics, The University of Melbourne, Australia Abstract This paper studies non-crossing geometric perfect matchings. Two such per- fect matchings are compatible if they have the same vertex set and their union is also non-crossing. Our first result states that for any two perfect match- ings M and M 0 of the same set of n points, for some k 2 O(log n), there 0 is a sequence of perfect matchings M = M0;M1;:::;Mk = M , such that each Mi is compatible with Mi+1. This improves the previous best bound of k ≤ n − 2. We then study the conjecture: every perfect matching with an even number of edges has an edge-disjoint compatible perfect matching.
    [Show full text]
  • A Note on Light Geometric Graphs
    A note on light geometric graphs Eyal Ackerman∗ Jacob Foxy Rom Pinchasiz March 19, 2012 Abstract Let G be a geometric graph on n vertices in general position in the plane. We say that G is k-light if no edge e of G has the property that each of the two open half-planes bounded by the line through e contains more than k edges of G. We extend the previous result in [1] and withp a shorter argument show that every k-light geometric graph on n vertices has at most O(n k) edges. This bound is best possible. Keywords: Geometric graphs, k-near bipartite. 1 Introduction Let G be an n-vertex geometric graph. That is, G is a graph drawn in the plane such that its vertices are distinct points and its edges are straight-line segments connecting corresponding vertices. It is usually assumed, as we will assume in this paper, that the set of vertices of G is in general position in the sense that no three of them lie on a line. A typical question in geometric graph theory asks for the maximum number of edges that a geometric graph on n vertices can have assuming a forbidden configuration in that graph. This is a popular area of study extending classical extremal graph theory, utilizing diverse tools from both geometry and combinatorics. For example, an old result of Hopf and Pannwitz [3] and independently Sutherland [7] states that any geometric graph on n vertices with no pair of disjoint edges has at most n edges.
    [Show full text]
  • 55 GRAPH DRAWING Emilio Di Giacomo, Giuseppe Liotta, Roberto Tamassia
    55 GRAPH DRAWING Emilio Di Giacomo, Giuseppe Liotta, Roberto Tamassia INTRODUCTION Graph drawing addresses the problem of constructing geometric representations of graphs, and has important applications to key computer technologies such as soft- ware engineering, database systems, visual interfaces, and computer-aided design. Research on graph drawing has been conducted within several diverse areas, includ- ing discrete mathematics (topological graph theory, geometric graph theory, order theory), algorithmics (graph algorithms, data structures, computational geometry, vlsi), and human-computer interaction (visual languages, graphical user interfaces, information visualization). This chapter overviews aspects of graph drawing that are especially relevant to computational geometry. Basic definitions on drawings and their properties are given in Section 55.1. Bounds on geometric and topological properties of drawings (e.g., area and crossings) are presented in Section 55.2. Sec- tion 55.3 deals with the time complexity of fundamental graph drawing problems. An example of a drawing algorithm is given in Section 55.4. Techniques for drawing general graphs are surveyed in Section 55.5. 55.1 DRAWINGS AND THEIR PROPERTIES TYPES OF GRAPHS First, we define some terminology on graphs pertinent to graph drawing. Through- out this chapter let n and m be the number of graph vertices and edges respectively, and d the maximum vertex degree (i.e., number of edges incident to a vertex). GLOSSARY Degree-k graph: Graph with maximum degree d k. ≤ Digraph: Directed graph, i.e., graph with directed edges. Acyclic digraph: Digraph without directed cycles. Transitive edge: Edge (u, v) of a digraph is transitive if there is a directed path from u to v not containing edge (u, v).
    [Show full text]
  • 10 GEOMETRIC GRAPH THEORY J´Anos Pach
    10 GEOMETRIC GRAPH THEORY J´anos Pach INTRODUCTION In the traditional areas of graph theory (Ramsey theory, extremal graph theory, random graphs, etc.), graphs are regarded as abstract binary relations. The relevant methods are often incapable of providing satisfactory answers to questions arising in geometric applications. Geometric graph theory focuses on combinatorial and geometric properties of graphs drawn in the plane by straight-line edges (or, more generally, by edges represented by simple Jordan arcs). It is a fairly new discipline abounding in open problems, but it has already yielded some striking results that have proved instrumental in the solution of several basic problems in combinatorial and computational geometry (including the k-set problem and metric questions discussed in Sections 1.1 and 1.2, respectively, of this Handbook). This chapter is partitioned into extremal problems (Section 10.1), crossing numbers (Section 10.2), and generalizations (Section 10.3). 10.1 EXTREMAL PROBLEMS Tur´an’s classical theorem [Tur54] determines the maximum number of edges that an abstract graph with n vertices can have without containing, as a subgraph, a complete graph with k vertices. In the spirit of this result, one can raise the follow- ing general question. Given a class of so-called forbidden geometric subgraphs, what is the maximum number of edgesH that a geometric graph of n vertices can have without containing a geometric subgraph belonging to ? Similarly, Ramsey’s the- orem [Ram30] for abstract graphs has some natural analoguesH for geometric graphs. In this section we will be concerned mainly with problems of these two types.
    [Show full text]
  • Spatial Distribution of Wireless Sensor Nodes in the Urban Environment
    Spatial Distribution of Wireless Sensor Nodes in the Urban Environment Vendula Hejlová Department of Geoinformatics, Palacký University Olomouc, 17. listopadu 50, Olomouc, Czech Republic Keywords: Wireless Sensor Node, Distribution of Nodes, Graph Theory. Abstract: Wireless sensor nodes are an important part of every wireless sensor network. If wireless sensor nodes have implemented or connected sensors, then they can be used for different types of measurements. These measurements can be carried out either in internal or external environment. Spatial distribution of sensor nodes in the urban environment is a crucial decision because on its basis the selected elements will be measured in the suggested places. It is necessary to choose localities where these measurements have the long term significance. Distribution of sensor nodes in the urban environment is determined by a lot of factors. These factors are related to technical parameters of nodes, terrain characteristics and parameters of measured elements. Distribution of sensor nodes is made on the basis of distribution algorithms or the sensor nodes are randomly spread to the area of interest. The graph theory is usually the background of distribution algorithms. This theory primarily does not take into account the characteristics of terrain and measured elements. This paper describes factors that influence the distribution of sensor nodes. The graphs that are used in the wireless sensor networks are described and the most suitable solution for implementing terrain characteristics is selected. 1 INTRODUCTION The author solves the problem of distribution of wireless sensor nodes in the urban environment on Spatial distribution of wireless sensor nodes in the the basis of suggested factors.
    [Show full text]
  • Local Structure Theorems for Erdos Renyi Graphs and Their Algorithmic Application
    Local Structure Theorems for Erd}os{R´enyi Graphs and their Algorithmic Applications Jan Dreier1, Philipp Kuinke1, Ba Le Xuan2, and Peter Rossmanith1 1 Theoretical Computer Science, Dept. of Computer Science, RWTH Aachen University, Aachen, Germany fdreier,kuinke,[email protected] 2 The Sirindhorn International Thai-German Graduate School of Engineering, King Mongkut's University of Technology North Bangkok, Thailand [email protected] Abstract. We analyze local properties of sparse Erd}os{R´enyi graphs, where d(n)=n is the edge probability. In particular we study the behav- ior of very short paths. For d(n) = no(1) we show that G(n; d(n)=n) has asymptotically almost surely (a.a.s.) bounded local treewidth and there- fore is a.a.s. nowhere dense. We also discover a new and simpler proof that G(n; d=n) has a.a.s. bounded expansion for constant d. The local struc- ture of sparse Erd}os{R´enyi graphs is very special: The r-neighborhood of a vertex is a tree with some additional edges, where the probabil- ity that there are m additional edges decreases with m. This implies efficient algorithms for subgraph isomorphism, in particular for finding subgraphs with small diameter. Finally, experiments suggest that pref- erential attachment graphs might have similar properties after deleting a small number of vertices. Keywords: graph theory, random graphs, sparse graphs, graph algo- rithms 1 Introduction One of the earliest and most intensively studied random graph models is the Erd}os{R´enyi model [1,2]. Graphs from this class are usually depicted as a random variable G(n; p), which is a graph consisting of n vertices where each pair of vertices is connected independently uniformly at random with probability p.
    [Show full text]