146,585 research outputs found

    Shortest-weight paths in random regular graphs

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    Consider a random regular graph with degree dd and of size nn. Assign to each edge an i.i.d. exponential random variable with mean one. In this paper we establish a precise asymptotic expression for the maximum number of edges on the shortest-weight paths between a fixed vertex and all the other vertices, as well as between any pair of vertices. Namely, for any fixed d3d \geq 3, we show that the longest of these shortest-weight paths has about α^logn\hat{\alpha}\log n edges where α^\hat{\alpha} is the unique solution of the equation αlog(d2d1α)α=d3d2\alpha \log(\frac{d-2}{d-1}\alpha) - \alpha = \frac{d-3}{d-2}, for α>d1d2\alpha > \frac{d-1}{d-2}.Comment: 20 pages. arXiv admin note: text overlap with arXiv:1112.633

    Parameterized Aspects of Strong Subgraph Closure

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    Motivated by the role of triadic closures in social networks, and the importance of finding a maximum subgraph avoiding a fixed pattern, we introduce and initiate the parameterized study of the Strong F-closure problem, where F is a fixed graph. This is a generalization of Strong Triadic Closure, whereas it is a relaxation of F-free Edge Deletion. In Strong F-closure, we want to select a maximum number of edges of the input graph G, and mark them as strong edges, in the following way: whenever a subset of the strong edges forms a subgraph isomorphic to F, then the corresponding induced subgraph of G is not isomorphic to F. Hence the subgraph of G defined by the strong edges is not necessarily F-free, but whenever it contains a copy of F, there are additional edges in G to destroy that strong copy of F in G. We study Strong F-closure from a parameterized perspective with various natural parameterizations. Our main focus is on the number k of strong edges as the parameter. We show that the problem is FPT with this parameterization for every fixed graph F, whereas it does not admit a polynomial kernel even when F =P_3. In fact, this latter case is equivalent to the Strong Triadic Closure problem, which motivates us to study this problem on input graphs belonging to well known graph classes. We show that Strong Triadic Closure does not admit a polynomial kernel even when the input graph is a split graph, whereas it admits a polynomial kernel when the input graph is planar, and even d-degenerate. Furthermore, on graphs of maximum degree at most 4, we show that Strong Triadic Closure is FPT with the above guarantee parameterization k - mu(G), where mu(G) is the maximum matching size of G. We conclude with some results on the parameterization of Strong F-closure by the number of edges of G that are not selected as strong

    Parameterized Complexity of Simultaneous Planarity

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    Given kk input graphs G1,,GkG_1, \dots ,G_k, where each pair GiG_i, GjG_j with iji \neq j shares the same graph GG, the problem Simultaneous Embedding With Fixed Edges (SEFE) asks whether there exists a planar drawing for each input graph such that all drawings coincide on GG. While SEFE is still open for the case of two input graphs, the problem is NP-complete for k3k \geq 3 [Schaefer, JGAA 13]. In this work, we explore the parameterized complexity of SEFE. We show that SEFE is FPT with respect to kk plus the vertex cover number or the feedback edge set number of the the union graph G=G1GkG^\cup = G_1 \cup \dots \cup G_k. Regarding the shared graph GG, we show that SEFE is NP-complete, even if GG is a tree with maximum degree 4. Together with a known NP-hardness reduction [Angelini et al., TCS 15], this allows us to conclude that several parameters of GG, including the maximum degree, the maximum number of degree-1 neighbors, the vertex cover number, and the number of cutvertices are intractable. We also settle the tractability of all pairs of these parameters. We give FPT algorithms for the vertex cover number plus either of the first two parameters and for the number of cutvertices plus the maximum degree, whereas we prove all remaining combinations to be intractable.Comment: Appears in the Proceedings of the 31st International Symposium on Graph Drawing and Network Visualization (GD 2023

    On the Parameterized Complexity of Sparsest Cut and Small-set Expansion Problems

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    We study the NP-hard \textsc{kk-Sparsest Cut} problem (kkSC) in which, given an undirected graph G=(V,E)G = (V, E) and a parameter kk, the objective is to partition vertex set into kk subsets whose maximum edge expansion is minimized. Herein, the edge expansion of a subset SVS \subseteq V is defined as the sum of the weights of edges exiting SS divided by the number of vertices in SS. Another problem that has been investigated is \textsc{kk-Small-Set Expansion} problem (kkSSE), which aims to find a subset with minimum edge expansion with a restriction on the size of the subset. We extend previous studies on kkSC and kkSSE by inspecting their parameterized complexity. On the positive side, we present two FPT algorithms for both kkSSE and 2SC problems where in the first algorithm we consider the parameter treewidth of the input graph and uses exponential space, and in the second we consider the parameter vertex cover number of the input graph and uses polynomial space. Moreover, we consider the unweighted version of the kkSC problem where k2k \geq 2 is fixed and proposed two FPT algorithms with parameters treewidth and vertex cover number of the input graph. We also propose a randomized FPT algorithm for kkSSE when parameterized by kk and the maximum degree of the input graph combined. Its derandomization is done efficiently. \noindent On the negative side, first we prove that for every fixed integer k,τ3k,\tau\geq 3, the problem kkSC is NP-hard for graphs with vertex cover number at most τ\tau. We also show that kkSC is W[1]-hard when parameterized by the treewidth of the input graph and the number~kk of components combined using a reduction from \textsc{Unary Bin Packing}. Furthermore, we prove that kkSC remains NP-hard for graphs with maximum degree three and also graphs with degeneracy two. Finally, we prove that the unweighted kkSSE is W[1]-hard for the parameter kk

    Complexity of planar signed graph homomorphisms to cycles

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    We study homomorphism problems of signed graphs. A signed graph is an undirected graph where each edge is given a sign, positive or negative. An important concept for signed graphs is the operation of switching at a vertex, which is to change the sign of each incident edge. A homomorphism of a graph is a vertex-mapping that preserves the adjacencies; in the case of signed graphs, we also preserve the edge-signs. Special homomorphisms of signed graphs, called s-homomorphisms, have been studied. In an s-homomorphism, we allow, before the mapping, to perform any number of switchings on the source signed graph. This concept has been extensively studied, and a full complexity classification (polynomial or NP-complete) for s-homomorphism to a fixed target signed graph has recently been obtained. Such a dichotomy is not known when we restrict the input graph to be planar (not even for non-signed graph homomorphisms). We show that deciding whether a (non-signed) planar graph admits a homomorphism to the square Ct2C_t^2 of a cycle with t6t\ge 6, or to the circular clique K4t/(2t1)K_{4t/(2t-1)} with t2t\ge2, are NP-complete problems. We use these results to show that deciding whether a planar signed graph admits an s-homomorphism to an unbalanced even cycle is NP-complete. (A cycle is unbalanced if it has an odd number of negative edges). We deduce a complete complexity dichotomy for the planar s-homomorphism problem with any signed cycle as a target. We also study further restrictions involving the maximum degree and the girth of the input signed graph. We prove that planar s-homomorphism problems to signed cycles remain NP-complete even for inputs of maximum degree~33 (except for the case of unbalanced 44-cycles, for which we show this for maximum degree~44). We also show that for a given integer gg, the problem for signed bipartite planar inputs of girth gg is either trivial or NP-complete.Comment: 17 pages, 10 figure
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