57,060 research outputs found

    The Effect of Planarization on Width

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    We study the effects of planarization (the construction of a planar diagram DD from a non-planar graph GG by replacing each crossing by a new vertex) on graph width parameters. We show that for treewidth, pathwidth, branchwidth, clique-width, and tree-depth there exists a family of nn-vertex graphs with bounded parameter value, all of whose planarizations have parameter value Ω(n)\Omega(n). However, for bandwidth, cutwidth, and carving width, every graph with bounded parameter value has a planarization of linear size whose parameter value remains bounded. The same is true for the treewidth, pathwidth, and branchwidth of graphs of bounded degree.Comment: 15 pages, 6 figures. To appear at the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

    Convexity in partial cubes: the hull number

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    We prove that the combinatorial optimization problem of determining the hull number of a partial cube is NP-complete. This makes partial cubes the minimal graph class for which NP-completeness of this problem is known and improves some earlier results in the literature. On the other hand we provide a polynomial-time algorithm to determine the hull number of planar partial cube quadrangulations. Instances of the hull number problem for partial cubes described include poset dimension and hitting sets for interiors of curves in the plane. To obtain the above results, we investigate convexity in partial cubes and characterize these graphs in terms of their lattice of convex subgraphs, improving a theorem of Handa. Furthermore we provide a topological representation theorem for planar partial cubes, generalizing a result of Fukuda and Handa about rank three oriented matroids.Comment: 19 pages, 4 figure

    The Complexity of Drawing Graphs on Few Lines and Few Planes

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    It is well known that any graph admits a crossing-free straight-line drawing in R3\mathbb{R}^3 and that any planar graph admits the same even in R2\mathbb{R}^2. For a graph GG and d{2,3}d \in \{2,3\}, let ρd1(G)\rho^1_d(G) denote the minimum number of lines in Rd\mathbb{R}^d that together can cover all edges of a drawing of GG. For d=2d=2, GG must be planar. We investigate the complexity of computing these parameters and obtain the following hardness and algorithmic results. - For d{2,3}d\in\{2,3\}, we prove that deciding whether ρd1(G)k\rho^1_d(G)\le k for a given graph GG and integer kk is R{\exists\mathbb{R}}-complete. - Since NPR\mathrm{NP}\subseteq{\exists\mathbb{R}}, deciding ρd1(G)k\rho^1_d(G)\le k is NP-hard for d{2,3}d\in\{2,3\}. On the positive side, we show that the problem is fixed-parameter tractable with respect to kk. - Since RPSPACE{\exists\mathbb{R}}\subseteq\mathrm{PSPACE}, both ρ21(G)\rho^1_2(G) and ρ31(G)\rho^1_3(G) are computable in polynomial space. On the negative side, we show that drawings that are optimal with respect to ρ21\rho^1_2 or ρ31\rho^1_3 sometimes require irrational coordinates. - Let ρ32(G)\rho^2_3(G) be the minimum number of planes in R3\mathbb{R}^3 needed to cover a straight-line drawing of a graph GG. We prove that deciding whether ρ32(G)k\rho^2_3(G)\le k is NP-hard for any fixed k2k \ge 2. Hence, the problem is not fixed-parameter tractable with respect to kk unless P=NP\mathrm{P}=\mathrm{NP}

    Vertex Sparsifiers: New Results from Old Techniques

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    Given a capacitated graph G=(V,E)G = (V,E) and a set of terminals KVK \subseteq V, how should we produce a graph HH only on the terminals KK so that every (multicommodity) flow between the terminals in GG could be supported in HH with low congestion, and vice versa? (Such a graph HH is called a flow-sparsifier for GG.) What if we want HH to be a "simple" graph? What if we allow HH to be a convex combination of simple graphs? Improving on results of Moitra [FOCS 2009] and Leighton and Moitra [STOC 2010], we give efficient algorithms for constructing: (a) a flow-sparsifier HH that maintains congestion up to a factor of O(logk/loglogk)O(\log k/\log \log k), where k=Kk = |K|, (b) a convex combination of trees over the terminals KK that maintains congestion up to a factor of O(logk)O(\log k), and (c) for a planar graph GG, a convex combination of planar graphs that maintains congestion up to a constant factor. This requires us to give a new algorithm for the 0-extension problem, the first one in which the preimages of each terminal are connected in GG. Moreover, this result extends to minor-closed families of graphs. Our improved bounds immediately imply improved approximation guarantees for several terminal-based cut and ordering problems.Comment: An extended abstract appears in the 13th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2010. Final version to appear in SIAM J. Computin

    Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Edge Crossings

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    We provide linear-time algorithms for geometric graphs with sublinearly many crossings. That is, we provide algorithms running in O(n) time on connected geometric graphs having n vertices and k crossings, where k is smaller than n by an iterated logarithmic factor. Specific problems we study include Voronoi diagrams and single-source shortest paths. Our algorithms all run in linear time in the standard comparison-based computational model; hence, we make no assumptions about the distribution or bit complexities of edge weights, nor do we utilize unusual bit-level operations on memory words. Instead, our algorithms are based on a planarization method that "zeroes in" on edge crossings, together with methods for extending planar separator decompositions to geometric graphs with sublinearly many crossings. Incidentally, our planarization algorithm also solves an open computational geometry problem of Chazelle for triangulating a self-intersecting polygonal chain having n segments and k crossings in linear time, for the case when k is sublinear in n by an iterated logarithmic factor.Comment: Expanded version of a paper appearing at the 20th ACM-SIAM Symposium on Discrete Algorithms (SODA09
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