158 research outputs found

    Rainbow domination and related problems on some classes of perfect graphs

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    Let kNk \in \mathbb{N} and let GG be a graph. A function f:V(G)2[k]f: V(G) \rightarrow 2^{[k]} is a rainbow function if, for every vertex xx with f(x)=f(x)=\emptyset, f(N(x))=[k]f(N(x)) =[k]. The rainbow domination number γkr(G)\gamma_{kr}(G) is the minimum of xV(G)f(x)\sum_{x \in V(G)} |f(x)| over all rainbow functions. We investigate the rainbow domination problem for some classes of perfect graphs

    On the stable degree of graphs

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    We define the stable degree s(G) of a graph G by s(G)∈=∈ min max d (v), where the minimum is taken over all maximal independent sets U of G. For this new parameter we prove the following. Deciding whether a graph has stable degree at most k is NP-complete for every fixed k∈≥∈3; and the stable degree is hard to approximate. For asteroidal triple-free graphs and graphs of bounded asteroidal number the stable degree can be computed in polynomial time. For graphs in these classes the treewidth is bounded from below and above in terms of the stable degree

    Space Efficient Algorithms for Breadth-Depth Search

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    Continuing the recent trend, in this article we design several space-efficient algorithms for two well-known graph search methods. Both these search methods share the same name {\it breadth-depth search} (henceforth {\sf BDS}), although they work entirely in different fashion. The classical implementation for these graph search methods takes O(m+n)O(m+n) time and O(nlgn)O(n \lg n) bits of space in the standard word RAM model (with word size being Θ(lgn)\Theta(\lg n) bits), where mm and nn denotes the number of edges and vertices of the input graph respectively. Our goal here is to beat the space bound of the classical implementations, and design o(nlgn)o(n \lg n) space algorithms for these search methods by paying little to no penalty in the running time. Note that our space bounds (i.e., with o(nlgn)o(n \lg n) bits of space) do not even allow us to explicitly store the required information to implement the classical algorithms, yet our algorithms visits and reports all the vertices of the input graph in correct order.Comment: 12 pages, This work will appear in FCT 201

    Line-distortion, Bandwidth and Path-length of a graph

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    We investigate the minimum line-distortion and the minimum bandwidth problems on unweighted graphs and their relations with the minimum length of a Robertson-Seymour's path-decomposition. The length of a path-decomposition of a graph is the largest diameter of a bag in the decomposition. The path-length of a graph is the minimum length over all its path-decompositions. In particular, we show: - if a graph GG can be embedded into the line with distortion kk, then GG admits a Robertson-Seymour's path-decomposition with bags of diameter at most kk in GG; - for every class of graphs with path-length bounded by a constant, there exist an efficient constant-factor approximation algorithm for the minimum line-distortion problem and an efficient constant-factor approximation algorithm for the minimum bandwidth problem; - there is an efficient 2-approximation algorithm for computing the path-length of an arbitrary graph; - AT-free graphs and some intersection families of graphs have path-length at most 2; - for AT-free graphs, there exist a linear time 8-approximation algorithm for the minimum line-distortion problem and a linear time 4-approximation algorithm for the minimum bandwidth problem

    On retracts, absolute retracts, and folds in cographs

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    Let G and H be two cographs. We show that the problem to determine whether H is a retract of G is NP-complete. We show that this problem is fixed-parameter tractable when parameterized by the size of H. When restricted to the class of threshold graphs or to the class of trivially perfect graphs, the problem becomes tractable in polynomial time. The problem is also soluble when one cograph is given as an induced subgraph of the other. We characterize absolute retracts of cographs.Comment: 15 page

    Independent Set Reconfiguration in Cographs

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    We study the following independent set reconfiguration problem, called TAR-Reachability: given two independent sets II and JJ of a graph GG, both of size at least kk, is it possible to transform II into JJ by adding and removing vertices one-by-one, while maintaining an independent set of size at least kk throughout? This problem is known to be PSPACE-hard in general. For the case that GG is a cograph (i.e. P4P_4-free graph) on nn vertices, we show that it can be solved in time O(n2)O(n^2), and that the length of a shortest reconfiguration sequence from II to JJ is bounded by 4n2k4n-2k, if such a sequence exists. More generally, we show that if XX is a graph class for which (i) TAR-Reachability can be solved efficiently, (ii) maximum independent sets can be computed efficiently, and which satisfies a certain additional property, then the problem can be solved efficiently for any graph that can be obtained from a collection of graphs in XX using disjoint union and complete join operations. Chordal graphs are given as an example of such a class XX

    Parameterized Edge Hamiltonicity

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    We study the parameterized complexity of the classical Edge Hamiltonian Path problem and give several fixed-parameter tractability results. First, we settle an open question of Demaine et al. by showing that Edge Hamiltonian Path is FPT parameterized by vertex cover, and that it also admits a cubic kernel. We then show fixed-parameter tractability even for a generalization of the problem to arbitrary hypergraphs, parameterized by the size of a (supplied) hitting set. We also consider the problem parameterized by treewidth or clique-width. Surprisingly, we show that the problem is FPT for both of these standard parameters, in contrast to its vertex version, which is W-hard for clique-width. Our technique, which may be of independent interest, relies on a structural characterization of clique-width in terms of treewidth and complete bipartite subgraphs due to Gurski and Wanke

    Analysing participatory video through the capability approach. A case study in Quart de Poblet (Valencia, Spain)

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    The aim of this paper is to analyse participatory video as a participatory action research method through the lenses of the capability approach. In order to do this, we used a participatory video experience that took place in the municipality of Quart de Poblet (Valencia, Spain) from February to March 2014. The participants were 11 young people between 16 and 24 years of age, severely affected by the economic crisis that has hit Spain in recent years. To develop our analysis, we introduced the participatory video as a technique and a process within the participatory action research methods. Then, we analysed the participatory process to verify the extent to which it had contributed to expanding the capabilities and agency of the participants. The evidence revealed a significant expansion of the awareness capability and, in some cases, of the capability for voice. In contrast, the capability to aspire and the agency of the participants were not expanded, due to contextual factors and the limitations of the process itself

    Fully dynamic recognition of proper circular-arc graphs

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    We present a fully dynamic algorithm for the recognition of proper circular-arc (PCA) graphs. The allowed operations on the graph involve the insertion and removal of vertices (together with its incident edges) or edges. Edge operations cost O(log n) time, where n is the number of vertices of the graph, while vertex operations cost O(log n + d) time, where d is the degree of the modified vertex. We also show incremental and decremental algorithms that work in O(1) time per inserted or removed edge. As part of our algorithm, fully dynamic connectivity and co-connectivity algorithms that work in O(log n) time per operation are obtained. Also, an O(\Delta) time algorithm for determining if a PCA representation corresponds to a co-bipartite graph is provided, where \Delta\ is the maximum among the degrees of the vertices. When the graph is co-bipartite, a co-bipartition of each of its co-components is obtained within the same amount of time.Comment: 60 pages, 15 figure
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