836 research outputs found

    Vertex arboricity of triangle-free graphs

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    Master's Project (M.S.) University of Alaska Fairbanks, 2016The vertex arboricity of a graph is the minimum number of colors needed to color the vertices so that the subgraph induced by each color class is a forest. In other words, the vertex arboricity of a graph is the fewest number of colors required in order to color a graph such that every cycle has at least two colors. Although not standard, we will refer to vertex arboricity simply as arboricity. In this paper, we discuss properties of chromatic number and k-defective chromatic number and how those properties relate to the arboricity of trianglefree graphs. In particular, we find bounds on the minimum order of a graph having arboricity three. Equivalently, we consider the largest possible vertex arboricity of triangle-free graphs of fixed order

    Graph Treewidth and Geometric Thickness Parameters

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    Consider a drawing of a graph GG in the plane such that crossing edges are coloured differently. The minimum number of colours, taken over all drawings of GG, is the classical graph parameter "thickness". By restricting the edges to be straight, we obtain the "geometric thickness". By further restricting the vertices to be in convex position, we obtain the "book thickness". This paper studies the relationship between these parameters and treewidth. Our first main result states that for graphs of treewidth kk, the maximum thickness and the maximum geometric thickness both equal k/2\lceil{k/2}\rceil. This says that the lower bound for thickness can be matched by an upper bound, even in the more restrictive geometric setting. Our second main result states that for graphs of treewidth kk, the maximum book thickness equals kk if k2k \leq 2 and equals k+1k+1 if k3k \geq 3. This refutes a conjecture of Ganley and Heath [Discrete Appl. Math. 109(3):215-221, 2001]. Analogous results are proved for outerthickness, arboricity, and star-arboricity.Comment: A preliminary version of this paper appeared in the "Proceedings of the 13th International Symposium on Graph Drawing" (GD '05), Lecture Notes in Computer Science 3843:129-140, Springer, 2006. The full version was published in Discrete & Computational Geometry 37(4):641-670, 2007. That version contained a false conjecture, which is corrected on page 26 of this versio

    Almost-Smooth Histograms and Sliding-Window Graph Algorithms

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    We study algorithms for the sliding-window model, an important variant of the data-stream model, in which the goal is to compute some function of a fixed-length suffix of the stream. We extend the smooth-histogram framework of Braverman and Ostrovsky (FOCS 2007) to almost-smooth functions, which includes all subadditive functions. Specifically, we show that if a subadditive function can be (1+ϵ)(1+\epsilon)-approximated in the insertion-only streaming model, then it can be (2+ϵ)(2+\epsilon)-approximated also in the sliding-window model with space complexity larger by factor O(ϵ1logw)O(\epsilon^{-1}\log w), where ww is the window size. We demonstrate how our framework yields new approximation algorithms with relatively little effort for a variety of problems that do not admit the smooth-histogram technique. For example, in the frequency-vector model, a symmetric norm is subadditive and thus we obtain a sliding-window (2+ϵ)(2+\epsilon)-approximation algorithm for it. Another example is for streaming matrices, where we derive a new sliding-window (2+ϵ)(\sqrt{2}+\epsilon)-approximation algorithm for Schatten 44-norm. We then consider graph streams and show that many graph problems are subadditive, including maximum submodular matching, minimum vertex-cover, and maximum kk-cover, thereby deriving sliding-window O(1)O(1)-approximation algorithms for them almost for free (using known insertion-only algorithms). Finally, we design for every d(1,2]d\in (1,2] an artificial function, based on the maximum-matching size, whose almost-smoothness parameter is exactly dd

    Vertex Arboricity of Toroidal Graphs with a Forbidden Cycle

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    The vertex arboricity a(G)a(G) of a graph GG is the minimum kk such that V(G)V(G) can be partitioned into kk sets where each set induces a forest. For a planar graph GG, it is known that a(G)3a(G)\leq 3. In two recent papers, it was proved that planar graphs without kk-cycles for some k{3,4,5,6,7}k\in\{3, 4, 5, 6, 7\} have vertex arboricity at most 2. For a toroidal graph GG, it is known that a(G)4a(G)\leq 4. Let us consider the following question: do toroidal graphs without kk-cycles have vertex arboricity at most 2? It was known that the question is true for k=3, and recently, Zhang proved the question is true for k=5k=5. Since a complete graph on 5 vertices is a toroidal graph without any kk-cycles for k6k\geq 6 and has vertex arboricity at least three, the only unknown case was k=4. We solve this case in the affirmative; namely, we show that toroidal graphs without 4-cycles have vertex arboricity at most 2.Comment: 8 pages, 2 figure
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