A polynomial method for the pos/neg weighted 3-median problem on a tree

Let a connected undirected graph G  =  (V, E) be given. In the classical p-median problem we want to find a set X containing p points in G such that the sum of weighted distances from X to all vertices in V is minimized. We consider the semi-obnoxious case where every vertex has either a positive or negative weight. In this case we have two different objective functions: the sum of the minimum weighted distances from X to all vertices and the sum of the weighted minimum distances. In this paper we show that for the case p = 3 an optimal solution for the second model in a tree can be found in O(n ⁵) time. If the 3-median is restricted to vertices or if the tree is a path then the complexity can be reduced to O(n ³). This research has partially been supported by the Spezialforschungsbereich F 003 “Optimierung und Kontrolle”, Projektbereich Diskrete Optimierung.     

Maximum-cover source location problems with objective edge-connectivity three

Given a graph G = (V, E), a set of vertices covers a vertex if the edge-connectivity between S and v is at least a given number k. Vertices in S are called sources. The maximum-cover source location problem, which is a variation of the source location problem, is to find a source set S with a given size at most p, maximizing the sum of the weight of vertices covered by S. In this paper, we show a polynomial-time algorithm for this problem in the case of k = 3 for a given undirected graph with a vertex weight function and an edge capacity function. Moreover, we show that this problem is NP-hard even if vertex weights and edge capacities are both uniform for general k.     

Finding a core of a tree with pos/neg weight

Let T?=?(V, E) be a tree. A core of T is a path P, for which the sum of the weighted distances from all vertices to this path is minimized. In this paper, we consider the semi-obnoxious case in which the vertices have positive or negative weights. We prove that, when the sum of the weights of vertices is negative, the core must be a single vertex and that, when the sum of the vertices?? weights is zero there exists a core that is a vertex. Morgan and Slater (J Algorithms 1:247?C258, 1980) presented a linear time algorithm to find the core of a tree with only positive weights of vertices. We show that their algorithm also works for semi-obnoxious problems.     

A New Bound on the Total Domination Subdivision Number

A set S of vertices of a graph G = (V, E) without isolated vertex is a total dominating set if every vertex of V(G) is adjacent to some vertex in S. The total domination number γ _t (G) is the minimum cardinality of a total dominating set of G. The total domination subdivision number is the minimum number of edges that must be subdivided (each edge in G can be subdivided at most once) in order to increase the total domination number. In this paper we prove that for every simple connected graph G of order n ≥ 3,

Bounds on Laplacian eigenvalues related to total and signed domination of graphs

A total dominating set in a graph G is a subset X of V (G) such that each vertex of V (G) is adjacent to at least one vertex of X. The total domination number of G is the minimum cardinality of a total dominating set. A function f: V (G) → {−1, 1} is a signed dominating function (SDF) if the sum of its function values over any closed neighborhood is at least one. The weight of an SDF is the sum of its function values over all vertices. The signed domination number of G is the minimum weight of an SDF on G. In this paper we present several upper bounds on the algebraic connectivity of a connected graph in terms of the total domination and signed domination numbers of the graph. Also, we give lower bounds on the Laplacian spectral radius of a connected graph in terms of the signed domination number of the graph.     

On super vertex-graceful unicyclic graphs

A graph G with p vertices and q edges, vertex set V(G) and edge set E(G), is said to be super vertex-graceful (in short SVG), if there exists a function pair (f, f ⁺) where f is a bijection from V(G) onto P, f ⁺ is a bijection from E(G) onto Q, f ⁺((u, v)) = f(u) + f(v) for any (u, v) ∈ E(G),

Disproof of a Conjecture on the Subdivision Domination Number of a Graph

A set S of vertices of a graph G = (V,E) is a dominating set if every vertex of is adjacent to some vertex in S. The domination number γ(G) is the minimum cardinality of a dominating set of G. The domination subdivision number sd_γ(G) is the minimum number of edges that must be subdivided (each edge in G can be subdivided at most once) in order to increase the domination number. Haynes et al. (Discussiones Mathematicae Graph Theory 21 (2001) 239-253) conjectured that for any graph G with . In this note we first give a counterexample to this conjecture in general and then we prove it for a particular class of graphs.     

On A Hypergraph Turán Problem Of Frankl

Let be the 2k-uniform hypergraph obtained by letting P₁, . . .,P_r be pairwise disjoint sets of size k and taking as edges all sets P_i∪P_j with i ≠ j. This can be thought of as the ‘k-expansion’ of the complete graph K_r: each vertex has been replaced with a set of size k. An example of a hypergraph with vertex set V that does not contain can be obtained by partitioning V = V1 ∪V₂ and taking as edges all sets of size 2k that intersect each of V₁ and V₂ in an odd number of elements. Let denote a hypergraph on n vertices obtained by this construction that has as many edges as possible. For n sufficiently large we prove a conjecture of Frankl, which states that any hypergraph on n vertices that contains no has at most as many edges as . Sidorenko has given an upper bound of for the Tur′an density of for any r, and a construction establishing a matching lower bound when r is of the form 2^p+1. In this paper we also show that when r=2^p+1, any -free hypergraph of density looks approximately like Sidorenko’s construction. On the other hand, when r is not of this form, we show that corresponding constructions do not exist and improve the upper bound on the Turán density of to , where c(r) is a constant depending only on r. The backbone of our arguments is a strategy of first proving approximate structure theorems, and then showing that any imperfections in the structure must lead to a suboptimal configuration. The tools for its realisation draw on extremal graph theory, linear algebra, the Kruskal–Katona theorem and properties of Krawtchouck polynomials. * Research supported in part by NSF grants DMS-0355497, DMS-0106589, and by an Alfred P. Sloan fellowship.     

Packing Trees with Constraints on the Leaf Degree

If m is a positive integer then we call a tree on at least 2 vertices an m-tree if no vertex is adjacent to more than m leaves. Kaneko proved that a connected, undirected graph G = (V, E) has a spanning m-tree if and only if for every the number of isolated vertices of G − X is at most —unless we have the exceptional case of and m = 1. As an attempt to integrate this result into the theory of graph packings, in this paper we consider the problem of packing a graph with m-trees. We use an approach different from that of Kaneko, and we deduce Gallai–Edmonds and Berge–Tutte type theorems and a matroidal result for the m-tree packing problem. Jácint Szabó: Research is supported by OTKA grants K60802, TS049788 and by European MCRTN Adonet, Contract Grant No.504438.     

Efficient and Practical Algorithms for Sequential Modular Decomposition

A module of an undirected graph G = (V, E) is a set X of vertices that have the same set of neighbors in V\X. The modular decomposition is a unique decomposition of the vertices into nested modules. We give a practical algorithm with an O(n + mα(m, n)) time bound and a variant with a linear time bound.     

On minimum metric dimension of honeycomb networks

A minimum metric basis is a minimum set W of vertices of a graph G(V,E) such that for every pair of vertices u and v of G, there exists a vertex wW with the condition that the length of a shortest path from u to w is different from the length of a shortest path from v to w. The honeycomb and hexagonal networks are popular mesh-derived parallel architectures. Using the duality of these networks we determine minimum metric bases for hexagonal and honeycomb networks.     

Nonstable <Emphasis Type="Italic">K</Emphasis>-theory for Graph Algebras

We compute the monoid V(L _K (E)) of isomorphism classes of finitely generated projective modules over certain graph algebras L _K (E), and we show that this monoid satisfies the refinement property and separative cancellation. We also show that there is a natural isomorphism between the lattice of graded ideals of L _K (E) and the lattice of order-ideals of V(L _K (E)). When K is the field of complex numbers, the algebra is a dense subalgebra of the graph C ^*-algebra C ^*(E), and we show that the inclusion map induces an isomorphism between the corresponding monoids. As a consequence, the graph C*-algebra of any row-finite graph turns out to satisfy the stable weak cancellation property. The first author was partially supported by the DGI and European Regional Development Fund, jointly, through Project BFM2002-01390, the second and the third by the DGI and European Regional Development Fund, jointly, through Project MTM2004-00149 and by PAI III grant FQM-298 of the Junta de Andalucía. Also, the first and third authors are partially supported by the Comissionat per Universitats i Recerca de la Generalitat de Catalunya.     

On Super Restricted Edge Connectivity of Half Vertex Transitive Graphs

Let X =  (V, E) be a connected graph. Call X super restricted edge connected in short, sup-λ′, if F is a minimum edge set of X such that X − F is disconnected and every component of X − F has at least two vertices, then F is the set of edges adjacent to a certain edge with minimum edge degree in X. A bipartite graph is said to be half vertex transitive if its automorphism group is transitive on the sets of its bipartition. In this article, we show that every connected half vertex transitive graph X with n =  |V(X)| ≥  4 and X \ncong K_1,n-1{X \ncong K_{1,n-1}} is λ′-optimal. By studying the λ′-superatoms of X, we characterize sup-λ′ connected half vertex transitive graphs. As a corollary, sup-λ′ connected Bi-Cayley graphs are also characterized.     

Medians of arbitrary graphs

For each vertex u in a connected graph H, the distance of u is the sum of the distances from u to each of the vertices v of H. A vertex of minimum distance in H is called a median vertex. It is shown that for any graph G there exists a graph H for which the subgraph of H induced by the median vertices is isomorphic to G.     

Efficient algorithms for a constrained k-tree core problem in a tree network

Let T=(V,E) be a free tree in which each vertex has a weight and each edge has a length. Let n=|V|. Given T and parameters k and l, a (k,l)-tree core is a subtree X of T with diameter l, having k leaves, which minimizes the sum of the weighted distances from all vertices in T to X. In this paper, two efficient algorithms are presented for finding a (k,l)-tree core of T. The first algorithm has O(n²) time complexity for the case that each edge has an arbitrary length. The second algorithm has O(lkn) time complexity for the case that the lengths of all edges are 1. The (k,l)-tree core problem has an application in distributed database systems.     

Perfect graph decompositions

Upper and lower bounds are given for the minimum number of perfect (induced or partial) subgraphs covering or partitioning the vertex set or the edge set of a graph ofn vertices. Also, weighted versions of the problems are investigated.Research supported in part by the OTKA Research Fund of the Hungarian Academy of Sciences, Grant No. 1812.     

J-means and I-means for minimum sum-of-squares clustering on networks

Given a graph, the Edge minimum sum-of-squares clustering problem requires finding p prototypes (cluster centres) by minimizing the sum of their squared distances from a set of vertices to their nearest prototype, where a prototype can be either a vertex or an inner point of an edge. In this paper we have implemented Variable neighborhood search based heuristic for solving it. We consider three different local search procedures, K-means, J-means, and a new I-means heuristic. Experimental results indicate that the implemented VNS-based heuristic produces the best known results in the literature.     

Antimagic labelling of vertex weighted graphs

Suppose G is a graph, k is a non‐negative integer. We say G is k‐antimagic if there is an injection f: E→{1, 2, …, |E| + k} such that for any two distinct vertices u and v, . We say G is weighted‐k‐antimagic if for any vertex weight function w: V→?, there is an injection f: E→{1, 2, …, |E| + k} such that for any two distinct vertices u and v, . A well‐known conjecture asserts that every connected graph G≠K₂ is 0‐antimagic. On the other hand, there are connected graphs G≠K₂ which are not weighted‐1‐antimagic. It is unknown whether every connected graph G≠K₂ is weighted‐2‐antimagic. In this paper, we prove that if G has a universal vertex, then G is weighted‐2‐antimagic. If G has a prime number of vertices and has a Hamiltonian path, then G is weighted‐1‐antimagic. We also prove that every connected graph G≠K₂ on n vertices is weighted‐ ?3n/2?‐antimagic. Copyright © 2011 Wiley Periodicals, Inc. J Graph Theory     

Paths with restricted degrees of their vertices in planar graphs

In this paper it is proved that every 3-connected planar graph contains a path on 3 vertices each of which is of degree at most 15 and a path on 4 vertices each of which has degree at most 23. Analogous results are stated for 3-connected planar graphs of minimum degree 4 and 5. Moreover, for every pair of integers n 3, k 4 there is a 2-connected planar graph such that every path on n vertices in it has a vertex of degree k.     

The number of edges of radius-invariant graphs

The eccentricity e(υ) of vertex υ is defined as a distance to a farthest vertex from υ. The radius of a graph G is defined as r(G) = {e(u)}. We consider properties of unchanging the radius of a graph under two different situations: deleting an arbitrary edge and deleting an arbitrary vertex. This paper gives the upper bounds for the number of edges in such graphs. Supported by VEGA grant No. 1/0084/08.