2,228 research outputs found

    Ricci-flat graphs with girth at least five

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    A graph is called Ricci-flat if its Ricci-curvatures vanish on all edges. Here we use the definition of Ricci-cruvature on graphs given in [Lin-Lu-Yau, Tohoku Math., 2011], which is a variation of [Ollivier, J. Funct. Math., 2009]. In this paper, we classified all Ricci-flat connected graphs with girth at least five: they are the infinite path, cycle CnC_n (nβ‰₯6n\geq 6), the dodecahedral graph, the Petersen graph, and the half-dodecahedral graph. We also construct many Ricci-flat graphs with girth 3 or 4 by using the root systems of simple Lie algebras.Comment: 14 pages, 15 figure

    Edge Roman domination on graphs

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    An edge Roman dominating function of a graph GG is a function f ⁣:E(G)β†’{0,1,2}f\colon E(G) \rightarrow \{0,1,2\} satisfying the condition that every edge ee with f(e)=0f(e)=0 is adjacent to some edge eβ€²e' with f(eβ€²)=2f(e')=2. The edge Roman domination number of GG, denoted by Ξ³Rβ€²(G)\gamma'_R(G), is the minimum weight w(f)=βˆ‘e∈E(G)f(e)w(f) = \sum_{e\in E(G)} f(e) of an edge Roman dominating function ff of GG. This paper disproves a conjecture of Akbari, Ehsani, Ghajar, Jalaly Khalilabadi and Sadeghian Sadeghabad stating that if GG is a graph of maximum degree Ξ”\Delta on nn vertices, then Ξ³Rβ€²(G)β‰€βŒˆΞ”Ξ”+1nβŒ‰\gamma_R'(G) \le \lceil \frac{\Delta}{\Delta+1} n \rceil. While the counterexamples having the edge Roman domination numbers 2Ξ”βˆ’22Ξ”βˆ’1n\frac{2\Delta-2}{2\Delta-1} n, we prove that 2Ξ”βˆ’22Ξ”βˆ’1n+22Ξ”βˆ’1\frac{2\Delta-2}{2\Delta-1} n + \frac{2}{2\Delta-1} is an upper bound for connected graphs. Furthermore, we provide an upper bound for the edge Roman domination number of kk-degenerate graphs, which generalizes results of Akbari, Ehsani, Ghajar, Jalaly Khalilabadi and Sadeghian Sadeghabad. We also prove a sharp upper bound for subcubic graphs. In addition, we prove that the edge Roman domination numbers of planar graphs on nn vertices is at most 67n\frac{6}{7}n, which confirms a conjecture of Akbari and Qajar. We also show an upper bound for graphs of girth at least five that is 2-cell embeddable in surfaces of small genus. Finally, we prove an upper bound for graphs that do not contain K2,3K_{2,3} as a subdivision, which generalizes a result of Akbari and Qajar on outerplanar graphs

    Excluded minors in cubic graphs

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    Let G be a cubic graph, with girth at least five, such that for every partition X,Y of its vertex set with |X|,|Y|>6 there are at least six edges between X and Y. We prove that if there is no homeomorphic embedding of the Petersen graph in G, and G is not one particular 20-vertex graph, then either G\v is planar for some vertex v, or G can be drawn with crossings in the plane, but with only two crossings, both on the infinite region. We also prove several other theorems of the same kind.Comment: 62 pages, 17 figure

    Modeling and synthesis of multicomputer interconnection networks

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    The type of interconnection network employed has a profound effect on the performance of a multicomputer and multiprocessor design. Adequate models are needed to aid in the design and development of interconnection networks. A novel modeling approach using statistical and optimization techniques is described. This method represents an attempt to compare diverse interconnection network designs in a way that allows not only the best of existing designs to be identified but to suggest other, perhaps hybrid, networks that may offer better performance. Stepwise linear regression is used to develop a polynomial surface representation of performance in a (k+1) space with a total of k quantitative and qualitative independent variables describing graph-theoretic characteristics such as size, average degree, diameter, radius, girth, node-connectivity, edge-connectivity, minimum dominating set size, and maximum number of prime node and edge cutsets. Dependent variables used to measure performance are average message delay and the ratio of message completion rate to network connection cost. Response Surface Methodology (RSM) optimizes a response variable from a polynomial function of several independent variables. Steepest ascent path may also be used to approach optimum points
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