5,749 research outputs found

    Rectangular Layouts and Contact Graphs

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    Contact graphs of isothetic rectangles unify many concepts from applications including VLSI and architectural design, computational geometry, and GIS. Minimizing the area of their corresponding {\em rectangular layouts} is a key problem. We study the area-optimization problem and show that it is NP-hard to find a minimum-area rectangular layout of a given contact graph. We present O(n)-time algorithms that construct O(n2)O(n^2)-area rectangular layouts for general contact graphs and O(nlogn)O(n\log n)-area rectangular layouts for trees. (For trees, this is an O(logn)O(\log n)-approximation algorithm.) We also present an infinite family of graphs (rsp., trees) that require Ω(n2)\Omega(n^2) (rsp., Ω(nlogn)\Omega(n\log n)) area. We derive these results by presenting a new characterization of graphs that admit rectangular layouts using the related concept of {\em rectangular duals}. A corollary to our results relates the class of graphs that admit rectangular layouts to {\em rectangle of influence drawings}.Comment: 28 pages, 13 figures, 55 references, 1 appendi

    A Note on Plus-Contacts, Rectangular Duals, and Box-Orthogonal Drawings

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    A plus-contact representation of a planar graph GG is called cc-balanced if for every plus shape +v+_v, the number of other plus shapes incident to each arm of +v+_v is at most cΔ+O(1) c \Delta +O(1), where Δ\Delta is the maximum degree of GG. Although small values of cc have been achieved for a few subclasses of planar graphs (e.g., 22- and 33-trees), it is unknown whether cc-balanced representations with c<1c<1 exist for arbitrary planar graphs. In this paper we compute (1/2)(1/2)-balanced plus-contact representations for all planar graphs that admit a rectangular dual. Our result implies that any graph with a rectangular dual has a 1-bend box-orthogonal drawings such that for each vertex vv, the box representing vv is a square of side length deg(v)2+O(1)\frac{deg(v)}{2}+ O(1).Comment: A poster related to this research appeared at the 25th International Symposium on Graph Drawing & Network Visualization (GD 2017

    On Semantic Word Cloud Representation

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    We study the problem of computing semantic-preserving word clouds in which semantically related words are close to each other. While several heuristic approaches have been described in the literature, we formalize the underlying geometric algorithm problem: Word Rectangle Adjacency Contact (WRAC). In this model each word is associated with rectangle with fixed dimensions, and the goal is to represent semantically related words by ensuring that the two corresponding rectangles touch. We design and analyze efficient polynomial-time algorithms for some variants of the WRAC problem, show that several general variants are NP-hard, and describe a number of approximation algorithms. Finally, we experimentally demonstrate that our theoretically-sound algorithms outperform the early heuristics

    Improved Compact Visibility Representation of Planar Graph via Schnyder's Realizer

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    Let GG be an nn-node planar graph. In a visibility representation of GG, each node of GG is represented by a horizontal line segment such that the line segments representing any two adjacent nodes of GG are vertically visible to each other. In the present paper we give the best known compact visibility representation of GG. Given a canonical ordering of the triangulated GG, our algorithm draws the graph incrementally in a greedy manner. We show that one of three canonical orderings obtained from Schnyder's realizer for the triangulated GG yields a visibility representation of GG no wider than 22n4015\frac{22n-40}{15}. Our easy-to-implement O(n)-time algorithm bypasses the complicated subroutines for four-connected components and four-block trees required by the best previously known algorithm of Kant. Our result provides a negative answer to Kant's open question about whether 3n62\frac{3n-6}{2} is a worst-case lower bound on the required width. Also, if GG has no degree-three (respectively, degree-five) internal node, then our visibility representation for GG is no wider than 4n93\frac{4n-9}{3} (respectively, 4n73\frac{4n-7}{3}). Moreover, if GG is four-connected, then our visibility representation for GG is no wider than n1n-1, matching the best known result of Kant and He. As a by-product, we obtain a much simpler proof for a corollary of Wagner's Theorem on realizers, due to Bonichon, Sa\"{e}c, and Mosbah.Comment: 11 pages, 6 figures, the preliminary version of this paper is to appear in Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science (STACS), Berlin, Germany, 200

    Pixel and Voxel Representations of Graphs

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    We study contact representations for graphs, which we call pixel representations in 2D and voxel representations in 3D. Our representations are based on the unit square grid whose cells we call pixels in 2D and voxels in 3D. Two pixels are adjacent if they share an edge, two voxels if they share a face. We call a connected set of pixels or voxels a blob. Given a graph, we represent its vertices by disjoint blobs such that two blobs contain adjacent pixels or voxels if and only if the corresponding vertices are adjacent. We are interested in the size of a representation, which is the number of pixels or voxels it consists of. We first show that finding minimum-size representations is NP-complete. Then, we bound representation sizes needed for certain graph classes. In 2D, we show that, for kk-outerplanar graphs with nn vertices, Θ(kn)\Theta(kn) pixels are always sufficient and sometimes necessary. In particular, outerplanar graphs can be represented with a linear number of pixels, whereas general planar graphs sometimes need a quadratic number. In 3D, Θ(n2)\Theta(n^2) voxels are always sufficient and sometimes necessary for any nn-vertex graph. We improve this bound to Θ(nτ)\Theta(n\cdot \tau) for graphs of treewidth τ\tau and to O((g+1)2nlog2n)O((g+1)^2n\log^2n) for graphs of genus gg. In particular, planar graphs admit representations with O(nlog2n)O(n\log^2n) voxels

    Guidance on the key skills units : communication, application of number and information technology

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