An algorithm to recognize a middle graph

A graph F is called middle if there exists a graph G such that there is a one-to-one correspondence between the vertices of F and the vertices and edges of G such that two vertices of F are adjacent if and only if the corresponding elements (considered as subsets of the set of vertices) have a non-empty intersection.In this paper we present a linear time algorithm for the recognition of the middle graphs. The algorithm is based on a computer-oriented characterization of middle graphs. We show also how the algorithm can be generalized to recognize the middle graphs of hypergraphs.     

A recognition algorithm for the intersection graphs of directed paths in directed trees

Consider a finite family of non-empty sets. The intersection graph of this family is obtained by representing each set by a vertex, two vertices being connected by an edge if and only if the corresponding sets intersect. The intersection graph of a family of directed paths in a directed tree is called a directed path graph. In this paper we present an efficient algorithm which constructs to a given graph a representation by a family of directed paths on a directed tree, if one exists. Also, we prove that a graph is a proper directed path graph if and only if it is a directed path graph.     

Computing the boxicity of a graph by covering its complement by cointerval graphs

If $\text{F}$ is a family of sets, its intersection graph has the sets in $\text{F}$ as vertices and an edge between two sets if and only if they overlap. This paper investigates the concept of boxicity of a graph G, the smallest n such that G is the intersection graph of boxes in Euclidean n-space. The boxicity, b(G), was introduced by Roberts in 1969 and has since been studied by Cohen, Gabai, and Trotter. The concept has applications to niche overlap (competition) in ecology and to problems of fleet maintenance in operations research. These applications will be described briefly. While the problem of computing boxicity is in general a difficult problem (it is NP-complete), this paper develops techniques for computing boxicity which give useful bounds. They are based on the simple observation that b(G)≤k if and only if there is an edge covering of $\text{G}$ by spanning subgraphs of $\text{G}$, each of which is a cointerval graph, the complement of an interval graph (a graph of boxicity ≤1.).     

The intersection graphs of subtrees in trees are exactly the chordal graphs

The intersection graph of a family of subtrees in an undirected tree is called a subtree graph. A graph is called chordal if every simple circuit with more than three vertices has an edge connecting two non-consecutive vertices. In this paper, we prove that, for a graph G, the following conditions are equivalent: (i) G is a chordal graph; (ii) G is a subtree graph; (iii) G is a proper subtree graph.Consider a chordal graph G. We give an efficient algorithm for constructing a representation of G by a family of subtrees in a tree.     

On hereditary clique-Helly self-clique graphs

A graph is clique-Helly if any family of mutually intersecting (maximal) cliques has non-empty intersection, and it is hereditary clique-Helly (HCH) if its induced subgraphs are clique-Helly. The clique graph of a graph G is the intersection graph of its cliques, and G is self-clique if it is connected and isomorphic to its clique graph. We show that every HCH graph is an induced subgraph of a self-clique HCH graph, and give a characterization of self-clique HCH graphs in terms of their constructibility starting from certain digraphs with some forbidden subdigraphs. We also specialize this results to involutive HCH graphs, i.e. self-clique HCH graphs whose vertex-clique bipartite graph admits a part-switching involution.     

Graphoidal bipartite graphs

A graphoidal cover of a graph G is a collection ψ of (not necessarily open) paths inG such that every path in ψ has at least two vertices, every vertex ofG is an internal vertex of at most one path in ψ and every edge of G is in exactly one path in ψ. Let Ω (ψ) denote the intersection graph of ψ. A graph G is said to be graphoidal if there exists a graphH and a graphoidal cover ψof H such that G is isomorphic to Ω(ψ). In this paper we study the properties of graphoidal graphs and obtain a forbidden subgraph characterisation of bipartite graphoidal graphs.     

On self-clique graphs with given clique sizes, II

The clique graph of a graph G is the graph obtained by taking the cliques of G as vertices, and two vertices are adjacent if and only if the corresponding cliques have a non-empty intersection. A graph is self-clique if it is isomorphic to its clique graph. We give a new characterization of the set of all connected self-clique graphs having all cliques but two of size 2.     

Clique-critical graphs: Maximum size and recognition

The clique graph of G, K(G), is the intersection graph of the family of cliques (maximal complete sets) of G. Clique-critical graphs were defined as those whose clique graph changes whenever a vertex is removed. We prove that if G has m edges then any clique-critical graph in K^-1(G) has at most 2m vertices, which solves a question posed by Escalante and Toft [On clique-critical graphs, J. Combin. Theory B 17 (1974) 170-182]. The proof is based on a restatement of their characterization of clique-critical graphs. Moreover, the bound is sharp. We also show that the problem of recognizing clique-critical graphs is NP-complete.     

Minimal configuration bicyclic graphs

The nullity η(G) of a graph G is the multiplicity of zero as an eigenvalue of the adjacency matrix of G. If η(G)?=?1, then the core of G is the subgraph induced by the vertices associated with the nonzero entries of the kernel eigenvector. The set of vertices which are not in the core is the periphery of G. A graph G with nullity one is minimal configuration if no two vertices in the periphery are adjacent and deletion of any vertex in the periphery increases the nullity. An ∞-graph ∞(p,?l,?q) is a graph obtained by joining two vertex-disjoint cycles C _p and C _q by a path of length l?≥?0. Let ?* be the class of bicyclic graphs with an ∞-graph as an induced subgraph. In this article, we characterize the graphs in ?* which are minimal configurations.     

Algorithms for Page Retrieval and Hamiltonian Paths on Forward-Convex Line Graphs

The problems of determining whether an undirected graph G is sequentially edge-connected, whether G has a dominating path, and whether there is a Hamiltonian path in the line graph of G are shown to be equivalent. These problems are NP-hard even if G is bipartite. They can be used to model certain page retrieval problems in relational database systems in which a graph called page connectivity graph is used to represent a join operation. Many page connectivity graphs encountered in practice are forward-convex. In this paper we develop an algorithm that solves the above-mentioned problems in O(|E(G)|) time for forward-convex graphs G.     

On detour homogeneous digraphs

If every vertex of a graph is an endvertex of a hamiltonian path, then the graph is called homogeneously traceable. If we require each vertex of a graph to be an endvertex of a longest path (not necessarily a hamiltonian path), then we call the graph a detour homogeneous graph. The concept of a homogeneously traceable graph was extended to digraphs by Bermond, Simões-Pereira, and C.M. Zamfirescu. Skupień introduced different classes of such digraphs. In this paper we discuss the extension of the concept of a detour homogeneous graph to digraphs.     

Dirichlet problem on locally finite graphs

In this paper, we study the existence and uniqueness of solutions to the vertex-weighted Dirichlet problem on locally finite graphs. Let B be a subset of the vertices of a graph G. The Dirichlet problem is to find a function whose discrete Laplacian on G?B and its values on B are given. Each infinite connected component of G?B is called an end of G relative to B. If there are no ends, then there is a unique solution to the Dirichlet problem. Such a solution can be obtained as a limit of an averaging process or as a minimizer of a certain functional or as a limit-solution of the heat equation on the graph. On the other hand, we show that if G is a locally finite graph with l ends, then the set of solutions of any Dirichlet problem, if non-empty, is at least l-dimensional.     

Tree 3-spanners in 2-sep directed path graphs: Characterization, recognition, and construction

A spanning tree T of a graph G is called a treet-spanner, if the distance between any two vertices in T is at most t-times their distance in G. A graph that has a tree t-spanner is called a treet-spanner admissible graph. The problem of deciding whether a graph is tree t-spanner admissible is NP-complete for any fixed t≥4, and is linearly solvable for t=1 and t=2. The case t=3 still remains open. A directed path graph is called a 2-sep directed path graph if all of its minimal a−b vertex separator for every pair of non-adjacent vertices a and b are of size two. Le and Le [H.-O. Le, V.B. Le, Optimal tree 3-spanners in directed path graphs, Networks 34 (2) (1999) 81-87] showed that directed path graphs admit tree 3-spanners. However, this result has been shown to be incorrect by Panda and Das [B.S. Panda, Anita Das, On tree 3-spanners in directed path graphs, Networks 50 (3) (2007) 203-210]. In fact, this paper observes that even the class of 2-sep directed path graphs, which is a proper subclass of directed path graphs, need not admit tree 3-spanners in general. It, then, presents a structural characterization of tree 3-spanner admissible 2-sep directed path graphs. Based on this characterization, a linear time recognition algorithm for tree 3-spanner admissible 2-sep directed path graphs is presented. Finally, a linear time algorithm to construct a tree 3-spanner of a tree 3-spanner admissible 2-sep directed path graph is proposed.     

A sufficient condition for hamiltonian circuits

A theorem is proved that is (in a sense) the best possible improvement on the following theme: If G is an undirected graph on n vertices in which $\text{|Γ(S)| ≥}\text{1}\text{3}\text{(n + | S | + 3)}$ for every non-empty subset S of the vertices of G, then G is Hamiltonian.     

Graph equations for line graphs,total graphs,middle graphs and quasi-total graphs

Let G be a graph with vertex-set V(G) and edge-set X(G). Let L(G) and T(G) denote the line graph and total graph of G. The middle graph M(G) of G is an intersection graph Ω(F) on the vertex-set V(G) of any graph G. Let F = V′(G) ∪ X(G) where V′(G) indicates the family of all one-point subsets of the set V(G), then $\text{M(G) = Ω(F)}$.The quasi-total graph P(G) of G is a graph with vertex-set V(G)∪X(G) and two vertices are adjacent if and only if they correspond to two non-adjacent vertices of G or to two adjacent edges of G or to a vertex and an edge incident to it in G.In this paper we solve graph equations $\text{L(G) ? P(H);}\text{L(G)}\text{? P(H); P(G) ? T(H);}\text{P(G)}\text{? T(H); M(G) ? P(H);}\text{M(G)}\text{? P(H)}$.     

The complexity of cover graph recognition for some varieties of finite lattices

We address the following decision problem: Instance: an undirected graphG. Problem: IsG a cover graph of a lattice? We prove that this problem is NP-complete. This extends results of Brightwell [5] and Ne?et?il and Rödl [12]. On the other hand, it follows from Alvarez theorem [2] that recognizing cover graphs of modular or distributive lattices is in P. An important tool in the proof of the first result is the following statement which may be of independent interest: Given an integerl, l?3, there exists an algorithm which for a graphG withn vertices yields, in time polynomial inn, a graphH with the number of vertices polynomial inn, and satisfying girth(H)?l and χ(H)=χ(G).     

Clique partitions of distance multigraphs

We consider the minimum number of cliques needed to partition the edge set of D(G), the distance multigraph of a simple graph G. Equivalently, we seek to minimize the number of elements needed to label the vertices of a simple graph G by sets so that the distance between two vertices equals the cardinality of the intersection of their labels. We use a fractional analogue of this parameter to find lower bounds for the distance multigraphs of various classes of graphs. Some of the bounds are shown to be exact.     

When is G 2 a König–Egerváry Graph?

The independence number of a graph G, denoted by α(G), is the cardinality of a maximum independent set, and μ(G) is the size of a maximum matching in G. If α(G) + μ(G) equals its order, then G is a König–Egerváry graph. The square of a graph G is the graph G ² with the same vertex set as in G, and an edge of G ² is joining two distinct vertices, whenever the distance between them in G is at most two. G is a square-stable graph if it enjoys the property α(G) = α(G ²). In this paper we show that G ² is a König–Egerváry graph if and only if G is a square-stable König–Egerváry graph.     

Graphs with a Minimal Number of Convex Sets

Let G be a graph with vertex set V(G). A set C of vertices of G is g-convex if for every pair \({u, v \in C}\) the vertices on every u–v geodesic (i.e. shortest u–v path) belong to C. If the only g-convex sets of G are the empty set, V(G), all singletons and all edges, then G is called a g-minimal graph. It is shown that a graph is g-minimal if and only if it is triangle-free and if it has the property that the convex hull of every pair of non-adjacent vertices is V(G). Several properties of g-minimal graphs are established and it is shown that every triangle-free graph is an induced subgraph of a g-minimal graph. Recursive constructions of g-minimal graphs are described and bounds for the number of edges in these graphs are given. It is shown that the roots of the generating polynomials of the number of g-convex sets of each size of a g-minimal graphs are bounded, in contrast to their behaviour over all graphs. A set C of vertices of a graph is m-convex if for every pair \({u, v \in C}\) , the vertices of every induced u–v path belong to C. A graph is m-minimal if it has no m-convex sets other than the empty set, the singletons, the edges and the entire vertex set. Sharp bounds on the number of edges in these graphs are given and graphs that are m-minimal are shown to be precisely the 2-connected, triangle-free graphs for which no pair of adjacent vertices forms a vertex cut-set.     

Polygon-circle and word-representable graphs

We describe work on the relationship between the independently-studied polygon-circle graphs and word-representable graphs.A graph G = (V, E) is word-representable if there exists a word w over the alpha-bet V such that letters x and y form a subword of the form xyxy ⋯ or yxyx ⋯ iff xy is an edge in E. Word-representable graphs generalise several well-known and well-studied classes of graphs [S. Kitaev, A Comprehensive Introduction to the Theory of Word-Representable Graphs, Lecture Notes in Computer Science 10396 (2017) 36–67; S. Kitaev, V. Lozin, “Words and Graphs”, Springer, 2015]. It is known that any word-representable graph is k-word-representable, that is, can be represented by a word having exactly k copies of each letter for some k dependent on the graph. Recognising whether a graph is word-representable is NP-complete ([S. Kitaev, V. Lozin, “Words and Graphs”, Springer, 2015, Theorem 4.2.15]). A polygon-circle graph (also known as a spider graph) is the intersection graph of a set of polygons inscribed in a circle [M. Koebe, On a new class of intersection graphs, Ann. Discrete Math. (1992) 141–143]. That is, two vertices of a graph are adjacent if their respective polygons have a non-empty intersection, and the set of polygons that correspond to vertices in this way are said to represent the graph. Recognising whether an input graph is a polygon-circle graph is NP-complete [M. Pergel, Recognition of polygon-circle graphs and graphs of interval filaments is NP-complete, Graph-Theoretic Concepts in Computer Science: 33rd Int. Workshop, Lecture Notes in Computer Science, 4769 (2007) 238–247]. We show that neither of these two classes is included in the other one by showing that the word-representable Petersen graph and crown graphs are not polygon-circle, while the non-word-representable wheel graph W₅ is polygon-circle. We also provide a more refined result showing that for any k ≥ 3, there are k-word-representable graphs which are neither (k −1)-word-representable nor polygon-circle.