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A graph G = ( V , E ) is a word-representable graph if there exists a word W over the alphabet V such that letters x and y alternate in W if and only if ( x , y ) ∈ E for each x ≠ y . In this paper we give an effective characterization of word-representable graphs in terms of orientations. Namely, we show that a graph is word-representable if and only if it admits a semi-transitive orientation defined in the paper. This allows us to prove a number of results about word-representable graphs, in particular showing that the recognition problem is in NP, and that word-representable graphs include all 3-colorable graphs. We also explore bounds on the size of the word representing the graph. The representation number of G is the minimum k such that G is a representable by a word, where each letter occurs k times; such a k exists for any word-representable graph. We show that the representation number of a word-representable graph on n vertices is at most 2 n , while there exist graphs for which it is n / 2 . [ABSTRACT FROM AUTHOR]

Diagnosability plays an important role in measuring the reliability of interconnection networks. Conditional faulty set is a special faulty set that does not contain all of neighbors of any vertex in a network. The conditional diagnosability is a metric that can give the maximum cardinality of the conditional faulty sets that the system is guaranteed to identify. This paper shows that the conditional diagnosability of the bubble-sort star graph B S n under the MM model is 6 n − 15 for n ≥ 6 and under the PMC model is 8 n − 21 for n ≥ 5 . [ABSTRACT FROM AUTHOR]

An orthogonal ray graph is an intersection graph of horizontal rays (closed half-lines) and vertical rays in the plane, which is introduced in connection with the defect-tolerant design of nano-circuits. An orthogonal ray graph is a 3-directional orthogonal ray graph if every vertical ray has the same direction. A 3-directional orthogonal ray graph is a 2-directional orthogonal ray graph if every horizontal ray has the same direction. The characterizations and the complexity of the recognition problem have been open for orthogonal ray graphs and 3-directional orthogonal ray graphs, while various characterizations with a quadratic-time recognition algorithm have been known for 2-directional orthogonal ray graphs. In this paper, we show several characterizations with a linear-time recognition algorithm for orthogonal ray trees by using the 2-directional orthogonal ray trees. We also show that a tree is a 3-directional orthogonal ray graph if and only if it is a 2-directional orthogonal ray graph. Moreover, we show some necessary conditions for orthogonal ray graphs and 3-directional orthogonal ray graphs. [ABSTRACT FROM AUTHOR]

A subset S of vertices of a graph G is called a k -path vertex cover if every path of order k in G contains at least one vertex from S . The cardinality of a minimum k -path vertex cover is called the k -path vertex cover number of a graph G , denoted by ψ k ( G ) . It is known that the minimum k -path vertex cover problem ( k -PVCP) is NP-hard for all k ≥ 2 . In this paper we consider the weighted version of a k -PVCP ( k -WPVCP), in which vertices are given weights, and the problem is to find a minimum weight set in G such that the graph obtained by deleting this set from G has no P k . This problem was briefly introduced by Tu and Zhou (2011) but has not been studied to much extent. We give solutions and efficient algorithms for the k -WPVCP for some special classes of graphs. In particular, for complete graphs and cycles, and most importantly an algorithm that computes the k -path vertex cover number of a tree with time complexity O ( k ⋅ | V ( G ) | ) is presented. [ABSTRACT FROM AUTHOR]

Abstract: Let be a graph with vertex set . A moplex of is both a clique and a module whose neighborhood is a minimal separator in or empty. A moplex ordering of is an ordered partition of for some integer into moplexes which are defined in the successive transitory elimination graphs, i.e., for , is a moplex of the graph induced by and induces a clique. In this paper we prove the terminal vertex by an execution of the lexicographical depth-first search (LexDFS for short) algorithm on belongs to a moplex whose vertices are numbered consecutively and further that the LexDFS algorithm on defines a moplex ordering of , which is similar to the result about the maximum cardinality search (MCS for short) algorithm on chordal graphs [J.R.S. Blair, B.W. Peyton, An introduction to chordal graphs and clique trees, IMA Volumes in Mathematics and its Applications, 56 (1993) pp. 1–30] and the result about the lexicographical breadth-first search (LexBFS for short) algorithm on general graphs [A. Berry, J.-P. Bordat, Separability generalizes Dirac’s theorem, Discrete Appl. Math., 84 (1998) 43–53]. As a corollary, we can obtain a simple algorithm on a chordal graph to generate all minimal separators and all maximal cliques. [Copyright &y& Elsevier]

Abstract: A graph is a square root of a graph if two vertices are adjacent in if and only if they are at distance one or two in . Computing a square root of a given graph is NP-hard, even when the input graph is restricted to be chordal. In this paper, we show that computing a square root can be done in linear time for a well-known subclass of chordal graphs, the class of trivially perfect graphs. This result is obtained by developing a structural characterization of graphs that have a split square root. We also develop linear time algorithms for determining whether a threshold graph given either by a degree sequence or by a separating structure has a square root. [Copyright &y& Elsevier]

Abstract: A sigraph is a graph in which each edge carries a value called its sign, denoted specially as . Given a sigraph , a new sigraph , called the common-edge sigraph of is that sigraph whose vertex-set is the set of pairs of adjacent edges in and two vertices of are adjacent if the corresponding pairs of adjacent edges of have exactly one edge in common, and the sign of the edge is the sign of the common edge. If all the edges of the sigraph carry + sign then is actually a graph and the corresponding common-edge sigraph is termed as the common-edge graph. In this paper, we characterize common-edge graph and common-edge sigraph and write an algorithm to obtain a corresponding common-edge root graph and common-edge root sigraph from a given common-edge graph and common-edge sigraph respectively. [Copyright &y& Elsevier]

Abstract: In this paper, we study the class of distance-hereditary comparability graphs, that is, those graphs which admit a transitive orientation and are completely decomposable with respect to the split decomposition. We provide a characterization based on a restricted number of forbidden subgraphs. We also provide further characterizations and one of them, based on the split decomposition, is used to devise a recognizing algorithm working in linear time. Finally, we show how to build distance-hereditary comparability graphs. [Copyright &y& Elsevier]

Abstract: Given a graph , a defensive alliance of is a set of vertices satisfying the condition that for each , at least half of the vertices in the closed neighborhood of are in . A defensive alliance is called global if every vertex in is adjacent to at least one member of the defensive alliance . The global defensive alliance number of , denoted , is the minimum size around all the global defensive alliances of . In this paper, we present an efficient algorithm to determine the global defensive alliance numbers of trees, and further give formulas to decide the global defensive alliance numbers of complete -ary trees for . We also establish upper bounds and lower bounds for and , and show that the bounds are sharp for certain . [Copyright &y& Elsevier]

Abstract: The measure and conquer approach has proven to be a powerful tool to analyse exact algorithms for combinatorial problems like Dominating Set and Independent Set. This approach is used in this paper to obtain a faster exact algorithm for Dominating Set. We obtain this algorithm by considering a series of branch and reduce algorithms. This series is the result of an iterative process in which a mathematical analysis of an algorithm in the series with measure and conquer results in a convex or quasiconvex programming problem. The solution, by means of a computer, to this problem not only gives a bound on the running time of the algorithm, but can also give an indication on where to look for a new reduction rule, often giving a new, possibly faster algorithm. As a result, we obtain an time and polynomial space algorithm. [Copyright &y& Elsevier]

Abstract: This paper revisits the ‘branchwidth territories’ of Kloks, Kratochvíl and Müller [T. Kloks, J. Kratochvíl, H. Müller, New branchwidth territories, in: 16th Ann. Symp. on Theoretical Aspect of Computer Science, STACS, in: Lecture Notes in Computer Science, vol. 1563, 1999, pp. 173–183] to provide a simpler proof, and a faster algorithm for computing the branchwidth of an interval graph. We also generalize the algorithm to the class of chordal graphs, albeit at the expense of exponential running time. Compliance with the ternary constraint of the branchwidth definition is facilitated by a simple new tool called -troikas: three sets of size at most each are a -troika of set , if any two have union . We give a straightforward algorithm, computing branchwidth for an interval graph on edges, vertices and maximal cliques. We also prove a conjecture of Mazoit [F. Mazoit, A general scheme for deciding the branchwidth, Technical Report RR2004-34, LIP — École Normale Supérieure de Lyon, 2004. http://www.ens-lyon.fr/LIP/Pub/Rapports/RR/RR2004/RR2004-34.pdf], by showing that branchwidth can be computed in polynomial time for a chordal graph given with a clique tree having a polynomial number of subtrees. [Copyright &y& Elsevier]

Abstract: A set cover for a set is a collection of special subsets whose union is . Given covers and for two sets, the set-cover difference problem is to construct a new cover for the elements covered by but not . Applications include testing equivalence of set covers and maintaining a set cover dynamically. In this paper, we solve the set-cover difference problem by defining a difference operation , which turns out to be a pseudocomplement on a distributive lattice. We give an algorithm for constructing this difference, and show how to implement the algorithm for two examples with applications in computer science: face covers on a hypercube, and rectangle covers on a grid. We derive an upper bound on the time complexity of the algorithm, and give upper and lower bounds on complexity for face covers and rectangle covers. [Copyright &y& Elsevier]