Strong Parameterized Deletion: Bipartite Graphs
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Counting Independent Sets in Graphs with Bounded Bipartite Pathwidth∗
Counting independent sets in graphs with bounded bipartite pathwidth∗ Martin Dyery Catherine Greenhillz School of Computing School of Mathematics and Statistics University of Leeds UNSW Sydney, NSW 2052 Leeds LS2 9JT, UK Australia [email protected] [email protected] Haiko M¨uller∗ School of Computing University of Leeds Leeds LS2 9JT, UK [email protected] 7 August 2019 Abstract We show that a simple Markov chain, the Glauber dynamics, can efficiently sample independent sets almost uniformly at random in polynomial time for graphs in a certain class. The class is determined by boundedness of a new graph parameter called bipartite pathwidth. This result, which we prove for the more general hardcore distribution with fugacity λ, can be viewed as a strong generalisation of Jerrum and Sinclair's work on approximately counting matchings, that is, independent sets in line graphs. The class of graphs with bounded bipartite pathwidth includes claw-free graphs, which generalise line graphs. We consider two further generalisations of claw-free graphs and prove that these classes have bounded bipartite pathwidth. We also show how to extend all our results to polynomially-bounded vertex weights. 1 Introduction There is a well-known bijection between matchings of a graph G and independent sets in the line graph of G. We will show that we can approximate the number of independent sets ∗A preliminary version of this paper appeared as [19]. yResearch supported by EPSRC grant EP/S016562/1 \Sampling in hereditary classes". zResearch supported by Australian Research Council grant DP190100977. 1 in graphs for which all bipartite induced subgraphs are well structured, in a sense that we will define precisely. -
Density Theorems for Bipartite Graphs and Related Ramsey-Type Results
Density theorems for bipartite graphs and related Ramsey-type results Jacob Fox Benny Sudakov Princeton UCLA and IAS Ramsey’s theorem Definition: r(G) is the minimum N such that every 2-edge-coloring of the complete graph KN contains a monochromatic copy of graph G. Theorem: (Ramsey-Erdos-Szekeres,˝ Erdos)˝ t/2 2t 2 ≤ r(Kt ) ≤ 2 . Question: (Burr-Erd˝os1975) How large is r(G) for a sparse graph G on n vertices? Ramsey numbers for sparse graphs Conjecture: (Burr-Erd˝os1975) For every d there exists a constant cd such that if a graph G has n vertices and maximum degree d, then r(G) ≤ cd n. Theorem: 1 (Chv´atal-R¨odl-Szemer´edi-Trotter 1983) cd exists. 2αd 2 (Eaton 1998) cd ≤ 2 . βd αd log2 d 3 (Graham-R¨odl-Ruci´nski2000) 2 ≤ cd ≤ 2 . Moreover, if G is bipartite, r(G) ≤ 2αd log d n. Density theorem for bipartite graphs Theorem: (F.-Sudakov) Let G be a bipartite graph with n vertices and maximum degree d 2 and let H be a bipartite graph with parts |V1| = |V2| = N and εN edges. If N ≥ 8dε−d n, then H contains G. Corollary: For every bipartite graph G with n vertices and maximum degree d, r(G) ≤ d2d+4n. (D. Conlon independently proved that r(G) ≤ 2(2+o(1))d n.) Proof: Take ε = 1/2 and H to be the graph of the majority color. Ramsey numbers for cubes Definition: d The binary cube Qd has vertex set {0, 1} and x, y are adjacent if x and y differ in exactly one coordinate. -
Constrained Representations of Map Graphs and Half-Squares
Constrained Representations of Map Graphs and Half-Squares Hoang-Oanh Le Berlin, Germany [email protected] Van Bang Le Universität Rostock, Institut für Informatik, Rostock, Germany [email protected] Abstract The square of a graph H, denoted H2, is obtained from H by adding new edges between two distinct vertices whenever their distance in H is two. The half-squares of a bipartite graph B = (X, Y, EB ) are the subgraphs of B2 induced by the color classes X and Y , B2[X] and B2[Y ]. For a given graph 2 G = (V, EG), if G = B [V ] for some bipartite graph B = (V, W, EB ), then B is a representation of G and W is the set of points in B. If in addition B is planar, then G is also called a map graph and B is a witness of G [Chen, Grigni, Papadimitriou. Map graphs. J. ACM, 49 (2) (2002) 127-138]. While Chen, Grigni, Papadimitriou proved that any map graph G = (V, EG) has a witness with at most 3|V | − 6 points, we show that, given a map graph G and an integer k, deciding if G admits a witness with at most k points is NP-complete. As a by-product, we obtain NP-completeness of edge clique partition on planar graphs; until this present paper, the complexity status of edge clique partition for planar graphs was previously unknown. We also consider half-squares of tree-convex bipartite graphs and prove the following complexity 2 dichotomy: Given a graph G = (V, EG) and an integer k, deciding if G = B [V ] for some tree-convex bipartite graph B = (V, W, EB ) with |W | ≤ k points is NP-complete if G is non-chordal dually chordal and solvable in linear time otherwise. -
Graph Theory
1 Graph Theory “Begin at the beginning,” the King said, gravely, “and go on till you come to the end; then stop.” — Lewis Carroll, Alice in Wonderland The Pregolya River passes through a city once known as K¨onigsberg. In the 1700s seven bridges were situated across this river in a manner similar to what you see in Figure 1.1. The city’s residents enjoyed strolling on these bridges, but, as hard as they tried, no residentof the city was ever able to walk a route that crossed each of these bridges exactly once. The Swiss mathematician Leonhard Euler learned of this frustrating phenomenon, and in 1736 he wrote an article [98] about it. His work on the “K¨onigsberg Bridge Problem” is considered by many to be the beginning of the field of graph theory. FIGURE 1.1. The bridges in K¨onigsberg. J.M. Harris et al., Combinatorics and Graph Theory , DOI: 10.1007/978-0-387-79711-3 1, °c Springer Science+Business Media, LLC 2008 2 1. Graph Theory At first, the usefulness of Euler’s ideas and of “graph theory” itself was found only in solving puzzles and in analyzing games and other recreations. In the mid 1800s, however, people began to realize that graphs could be used to model many things that were of interest in society. For instance, the “Four Color Map Conjec- ture,” introduced by DeMorgan in 1852, was a famous problem that was seem- ingly unrelated to graph theory. The conjecture stated that four is the maximum number of colors required to color any map where bordering regions are colored differently. -
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. -
Lecture 12 — October 10, 2017 1 Overview 2 Matchings in Bipartite Graphs
CS 388R: Randomized Algorithms Fall 2017 Lecture 12 | October 10, 2017 Prof. Eric Price Scribe: Shuangquan Feng, Xinrui Hua 1 Overview In this lecture, we will explore 1. Problem of finding matchings in bipartite graphs. 2. Hall's Theorem: sufficient and necessary condition for the existence of perfect matchings in bipartite graphs. 3. An algorithm for finding perfect matching in d-regular bipartite graphs when d = 2k which is based on the idea of Euler tour and has a time complexity of O(nd). 4. A randomized algorithm for finding perfect matchings in all d regular bipartite graphs which has an expected time complexity of O(n log n). 5. Problem of online bipartite matching. 2 Matchings in Bipartite Graphs Definition 1 (Bipartite Graph). A graph G = (V; E) is said to be bipartite if the vertex set V can be partitioned into 2 disjoint sets L and R so that any edge has one vertex in L and the other in R. Definition 2 (Matching). Given an undirected graph G = (V; E), a matching is a subset of edges M ⊆ E that have no endpoints in common. Definition 3 (Maximum Matching). Given an undirected graph G = (V; E), a maximum matching M is a matching of maximum size. Thus for any other matching M 0, we have that jMj ≥ jM 0j. Definition 4 (Perfect Matching). Given an bipartite graph G = (V; E), with the bipartition V = L [ R where jLj = jRj = n, a perfect matching is a maximum matching of size n. There are several well known algorithms for the problem of finding maximum matchings in bipartite graphs. -
Vertex Deletion Problems on Chordal Graphs∗†
Vertex Deletion Problems on Chordal Graphs∗† Yixin Cao1, Yuping Ke2, Yota Otachi3, and Jie You4 1 Department of Computing, Hong Kong Polytechnic University, Hong Kong, China [email protected] 2 Department of Computing, Hong Kong Polytechnic University, Hong Kong, China [email protected] 3 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan [email protected] 4 School of Information Science and Engineering, Central South University and Department of Computing, Hong Kong Polytechnic University, Hong Kong, China [email protected] Abstract Containing many classic optimization problems, the family of vertex deletion problems has an important position in algorithm and complexity study. The celebrated result of Lewis and Yan- nakakis gives a complete dichotomy of their complexity. It however has nothing to say about the case when the input graph is also special. This paper initiates a systematic study of vertex deletion problems from one subclass of chordal graphs to another. We give polynomial-time algorithms or proofs of NP-completeness for most of the problems. In particular, we show that the vertex deletion problem from chordal graphs to interval graphs is NP-complete. 1998 ACM Subject Classification F.2.2 Analysis of Algorithms and Problem Complexity, G.2.2 Graph Theory Keywords and phrases vertex deletion problem, maximum subgraph, chordal graph, (unit) in- terval graph, split graph, hereditary property, NP-complete, polynomial-time algorithm Digital Object Identifier 10.4230/LIPIcs.FSTTCS.2017.22 1 Introduction Generally speaking, a vertex deletion problem asks to transform an input graph to a graph in a certain class by deleting a minimum number of vertices. -
Lecture 9-10: Extremal Combinatorics 1 Bipartite Forbidden Subgraphs 2 Graphs Without Any 4-Cycle
MAT 307: Combinatorics Lecture 9-10: Extremal combinatorics Instructor: Jacob Fox 1 Bipartite forbidden subgraphs We have seen the Erd}os-Stonetheorem which says that given a forbidden subgraph H, the extremal 1 2 number of edges is ex(n; H) = 2 (1¡1=(Â(H)¡1)+o(1))n . Here, o(1) means a term tending to zero as n ! 1. This basically resolves the question for forbidden subgraphs H of chromatic number at least 3, since then the answer is roughly cn2 for some constant c > 0. However, for bipartite forbidden subgraphs, Â(H) = 2, this answer is not satisfactory, because we get ex(n; H) = o(n2), which does not determine the order of ex(n; H). Hence, bipartite graphs form the most interesting class of forbidden subgraphs. 2 Graphs without any 4-cycle Let us start with the ¯rst non-trivial case where H is bipartite, H = C4. I.e., the question is how many edges G can have before a 4-cycle appears. The answer is roughly n3=2. Theorem 1. For any graph G on n vertices, not containing a 4-cycle, 1 p E(G) · (1 + 4n ¡ 3)n: 4 Proof. Let dv denote the degree of v 2 V . Let F denote the set of \labeled forks": F = f(u; v; w):(u; v) 2 E; (u; w) 2 E; v 6= wg: Note that we do not care whether (v; w) is an edge or not. We count the size of F in two possible ways: First, each vertex u contributes du(du ¡ 1) forks, since this is the number of choices for v and w among the neighbors of u. -
Hypergraph Packing and Sparse Bipartite Ramsey Numbers
Hypergraph packing and sparse bipartite Ramsey numbers David Conlon ∗ Abstract We prove that there exists a constant c such that, for any integer ∆, the Ramsey number of a bipartite graph on n vertices with maximum degree ∆ is less than 2c∆n. A probabilistic argument due to Graham, R¨odland Ruci´nskiimplies that this result is essentially sharp, up to the constant c in the exponent. Our proof hinges upon a quantitative form of a hypergraph packing result of R¨odl,Ruci´nskiand Taraz. 1 Introduction For a graph G, the Ramsey number r(G) is defined to be the smallest natural number n such that, in any two-colouring of the edges of Kn, there exists a monochromatic copy of G. That these numbers exist was proven by Ramsey [19] and rediscovered independently by Erd}osand Szekeres [10]. Whereas the original focus was on finding the Ramsey numbers of complete graphs, in which case it is known that t t p2 r(Kt) 4 ; ≤ ≤ the field has broadened considerably over the years. One of the most famous results in the area to date is the theorem, due to Chvat´al,R¨odl,Szemer´ediand Trotter [7], that if a graph G, on n vertices, has maximum degree ∆, then r(G) c(∆)n; ≤ where c(∆) is just some appropriate constant depending only on ∆. Their proof makes use of the regularity lemma and because of this the bound it gives on the constant c(∆) is (and is necessarily [11]) very bad. The situation was improved somewhat by Eaton [8], who proved, by using a variant of the regularity lemma, that the function c(∆) may be taken to be of the form 22c∆ . -
THE CRITICAL GROUP of a LINE GRAPH: the BIPARTITE CASE Contents 1. Introduction 2 2. Preliminaries 2 2.1. the Graph Laplacian 2
THE CRITICAL GROUP OF A LINE GRAPH: THE BIPARTITE CASE JOHN MACHACEK Abstract. The critical group K(G) of a graph G is a finite abelian group whose order is the number of spanning forests of the graph. Here we investigate the relationship between the critical group of a regular bipartite graph G and its line graph line G. The relationship between the two is known completely for regular nonbipartite graphs. We compute the critical group of a graph closely related to the complete bipartite graph and the critical group of its line graph. We also discuss general theory for the critical group of regular bipartite graphs. We close with various examples demonstrating what we have observed through experimentation. The problem of classifying the the relationship between K(G) and K(line G) for regular bipartite graphs remains open. Contents 1. Introduction 2 2. Preliminaries 2 2.1. The graph Laplacian 2 2.2. Theory of lattices 3 2.3. The line graph and edge subdivision graph 3 2.4. Circulant graphs 5 2.5. Smith normal form and matrices 6 3. Matrix reductions 6 4. Some specific regular bipartite graphs 9 4.1. The almost complete bipartite graph 9 4.2. Bipartite circulant graphs 11 5. A few general results 11 5.1. The quotient group 11 5.2. Perfect matchings 12 6. Looking forward 13 6.1. Odd primes 13 6.2. The prime 2 14 6.3. Example exact sequences 15 References 17 Date: December 14, 2011. A special thanks to Dr. Victor Reiner for his guidance and suggestions in completing this work. -
CS675: Convex and Combinatorial Optimization Spring 2018 Introduction to Matroid Theory
CS675: Convex and Combinatorial Optimization Spring 2018 Introduction to Matroid Theory Instructor: Shaddin Dughmi Set system: Pair (X ; I) where X is a finite ground set and I ⊆ 2X are the feasible sets Objective: often “linear”, referred to as modular Analogues of concave and convex: submodular and supermodular (in no particular order!) Today, we will look only at optimizing modular objectives over an extremely prolific family of set systems Related, directly or indirectly, to a large fraction of optimization problems in P Also pops up in submodular/supermodular optimization problems Optimization over Sets Most combinatorial optimization problems can be thought of as choosing the best set from a family of allowable sets Shortest paths Max-weight matching Independent set ... 1/30 Objective: often “linear”, referred to as modular Analogues of concave and convex: submodular and supermodular (in no particular order!) Today, we will look only at optimizing modular objectives over an extremely prolific family of set systems Related, directly or indirectly, to a large fraction of optimization problems in P Also pops up in submodular/supermodular optimization problems Optimization over Sets Most combinatorial optimization problems can be thought of as choosing the best set from a family of allowable sets Shortest paths Max-weight matching Independent set ... Set system: Pair (X ; I) where X is a finite ground set and I ⊆ 2X are the feasible sets 1/30 Analogues of concave and convex: submodular and supermodular (in no particular order!) Today, we will look only at optimizing modular objectives over an extremely prolific family of set systems Related, directly or indirectly, to a large fraction of optimization problems in P Also pops up in submodular/supermodular optimization problems Optimization over Sets Most combinatorial optimization problems can be thought of as choosing the best set from a family of allowable sets Shortest paths Max-weight matching Independent set .. -
Introducing Subclasses of Basic Chordal Graphs
Introducing subclasses of basic chordal graphs Pablo De Caria 1 Marisa Gutierrez 2 CONICET/ Universidad Nacional de La Plata, Argentina Abstract Basic chordal graphs arose when comparing clique trees of chordal graphs and compatible trees of dually chordal graphs. They were defined as those chordal graphs whose clique trees are exactly the compatible trees of its clique graph. In this work, we consider some subclasses of basic chordal graphs, like hereditary basic chordal graphs, basic DV and basic RDV graphs, we characterize them and we find some other properties they have, mostly involving clique graphs. Keywords: Chordal graph, dually chordal graph, clique tree, compatible tree. 1 Introduction 1.1 Definitions For a graph G, V (G) is the set of its vertices and E(G) is the set of its edges. A clique is a maximal set of pairwise adjacent vertices. The family of cliques of G is denoted by C(G). For v ∈ V (G), Cv will denote the family of cliques containing v. All graphs considered will be assumed to be connected. A set S ⊆ V (G) is a uv-separator if vertices u and v are in different connected components of G−S. It is minimal if no proper subset of S has the 1 Email: [email protected] aaURL: www.mate.unlp.edu.ar/~pdecaria 2 Email: [email protected] same property. S is a minimal vertex separator if there exist two nonadjacent vertices u and v such that S is a minimal uv-separator. S(G) will denote the family of all minimal vertex separators of G.