67,402 research outputs found

    A survey on algorithmic aspects of modular decomposition

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    The modular decomposition is a technique that applies but is not restricted to graphs. The notion of module naturally appears in the proofs of many graph theoretical theorems. Computing the modular decomposition tree is an important preprocessing step to solve a large number of combinatorial optimization problems. Since the first polynomial time algorithm in the early 70's, the algorithmic of the modular decomposition has known an important development. This paper survey the ideas and techniques that arose from this line of research

    OBDD-Based Representation of Interval Graphs

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    A graph G=(V,E)G = (V,E) can be described by the characteristic function of the edge set χE\chi_E which maps a pair of binary encoded nodes to 1 iff the nodes are adjacent. Using \emph{Ordered Binary Decision Diagrams} (OBDDs) to store χE\chi_E can lead to a (hopefully) compact representation. Given the OBDD as an input, symbolic/implicit OBDD-based graph algorithms can solve optimization problems by mainly using functional operations, e.g. quantification or binary synthesis. While the OBDD representation size can not be small in general, it can be provable small for special graph classes and then also lead to fast algorithms. In this paper, we show that the OBDD size of unit interval graphs is O( V /log V )O(\ | V \ | /\log \ | V \ |) and the OBDD size of interval graphs is $O(\ | V \ | \log \ | V \ |)whichbothimproveaknownresultfromNunkesserandWoelfel(2009).Furthermore,wecanshowthatusingourvariableorderandnodelabelingforintervalgraphstheworstcaseOBDDsizeis which both improve a known result from Nunkesser and Woelfel (2009). Furthermore, we can show that using our variable order and node labeling for interval graphs the worst-case OBDD size is \Omega(\ | V \ | \log \ | V \ |).Weusethestructureoftheadjacencymatricestoprovethesebounds.Thismethodmaybeofindependentinterestandcanbeappliedtoothergraphclasses.Wealsodevelopamaximummatchingalgorithmonunitintervalgraphsusing. We use the structure of the adjacency matrices to prove these bounds. This method may be of independent interest and can be applied to other graph classes. We also develop a maximum matching algorithm on unit interval graphs using O(\log \ | V \ |)operationsandacoloringalgorithmforunitandgeneralintervalsgraphsusing operations and a coloring algorithm for unit and general intervals graphs using O(\log^2 \ | V \ |)$ operations and evaluate the algorithms empirically.Comment: 29 pages, accepted for 39th International Workshop on Graph-Theoretic Concepts 201
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