2,548 research outputs found

    An extensive English language bibliography on graph theory and its applications, supplement 1

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    Graph theory and its applications - bibliography, supplement

    Evolutionary Inference via the Poisson Indel Process

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    We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classical evolutionary process, the TKF91 model, is a continuous-time Markov chain model comprised of insertion, deletion and substitution events. Unfortunately this model gives rise to an intractable computational problem---the computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a new stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The new model is closely related to the TKF91 model, differing only in its treatment of insertions, but the new model has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared to separate inference of phylogenies and alignments.Comment: 33 pages, 6 figure

    Rectilinear Steiner Tree Construction

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    The Minimum Rectilinear Steiner Tree (MRST) problem is to find the minimal spanning tree of a set of points (also called terminals) in the plane that interconnects all the terminals and some extra points (called Steiner points) introduced by intermediate junctions, and in which edge lengths are measured in the L1 (Manhattan) metric. This is one of the oldest optimization problems in mathematics that has been extensively studied and has been proven to be NP-complete, thus efficient approximation heuristics are more applicable than exact algorithms. In this thesis, we present a new heuristic to construct rectilinear Steiner trees (RSTs) with a close approximation of minimum length in Ο(n log n) time. To this end, we recursively divide a plane into a set of sub-planes of which optimal rectilinear Steiner trees (optRSTs) can be generated by a proposed exact algorithm called Const_optRST. By connecting all the optRSTs of the sub-planes, a sub-optimal MRST is eventually constructed. We show experimentally that for topologies with up to 100 terminals, the heuristic is 1.06 to 3.45 times faster than RMST, which is an efficient algorithm based on Prim’s method, with accuracy improvements varying from 1.31 % to 10.21 %

    Non-crossing of plane minimal spanning and minimal T1 networks

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    AbstractFor any given collection of Euclidean plane points it will be shown that a minimal length T1 network (or 3-size quasi Steiner network (Du et al., 1991)) will intersect a minimal spanning tree only at the given Euclidean points

    Listing Subgraphs by Cartesian Decomposition

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    We investigate a decomposition technique for listing problems in graphs and set systems. It is based on the Cartesian product of some iterators, which list the solutions of simpler problems. Our ideas applies to several problems, and we illustrate one of them in depth, namely, listing all minimum spanning trees of a weighted graph G. Here iterators over the spanning trees for unweighted graphs can be obtained by a suitable modification of the listing algorithm by [Shioura et al., SICOMP 1997], and the decomposition of G is obtained by suitably partitioning its edges according to their weights. By combining these iterators in a Cartesian product scheme that employs Gray coding, we give the first algorithm which lists all minimum spanning trees of G in constant delay, where the delay is the time elapsed between any two consecutive outputs. Our solution requires polynomial preprocessing time and uses polynomial space

    Mining and modeling graphs using patterns and priors

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