Vertex PI indices of four sums of graphs

Suppose that e is an edge of a graph G. Denote by m_e(G) the number of vertices of G that are not equidistant from both ends of e. Then the vertex PI index of G is defined as the summation of m_e(G) over all edges e of G. In this paper we give the explicit expressions for the vertex PI indices of four sums of two graphs in terms of other indices of two individual graphs, which correct the main results in a paper published in Ars Combin. 98 (2011).     

New upper bounds on harmonious colorings

We present an improved upper bound on the harmonious chromatic number of an arbitrary graph. We also consider ?fragmentable”? classes of graphs (an example is the class of planar graphs) that are, roughly speaking, graphs that can be decomposed into bounded-sized components by removing a small proportion of the vertices. We show that for such graphs of bounded degree the harmonious chromatic number is close to the lower bound (2m)^1/2, where m is the number of edges.     

Enumeration of Bipartite Self-Complementary Graphs

Let e(m, n), o(m, n), bsc(m, n) be the number of unlabelled bipartite graphs with an even number of edges whose partite sets have m vertices and n vertices, the number of those with an odd number of edges, and the number of unlabelled bipartite self-complementary graphs whose partite sets have m vertices and n vertices, respectively. Then this paper shows that the equality bsc(m, n) = e(m, n) ? o(m, n) holds.     

Circumference of 3-connected claw-free graphs and large Eulerian subgraphs of 3-edge-connected graphs

The circumference of a graph is the length of its longest cycles. Results of Jackson, and Jackson and Wormald, imply that the circumference of a 3-connected cubic n-vertex graph is Ω(n^0.694), and the circumference of a 3-connected claw-free graph is Ω(n^0.121). We generalize and improve the first result by showing that every 3-edge-connected graph with m edges has an Eulerian subgraph with Ω(m^0.753) edges. We use this result together with the Ryjá?ek closure operation to improve the lower bound on the circumference of a 3-connected claw-free graph to Ω(n^0.753). Our proofs imply polynomial time algorithms for finding large Eulerian subgraphs of 3-edge-connected graphs and long cycles in 3-connected claw-free graphs.     

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.     

Generating 3-vertex connected spanning subgraphs

In this paper we present an algorithm to generate all minimal 3-vertex connected spanning subgraphs of an undirected graph with n vertices and m edges in incremental polynomial time, i.e., for every K we can generate K (or all) minimal 3-vertex connected spanning subgraphs of a given graph in O(K²log(K)m²+K²m³) time, where n and m are the number of vertices and edges of the input graph, respectively. This is an improvement over what was previously available and is the same as the best known running time for generating 2-vertex connected spanning subgraphs. Our result is obtained by applying the decomposition theory of 2-vertex connected graphs to the graphs obtained from minimal 3-vertex connected graphs by removing a single edge.     

On reformulated Zagreb indices

The first and second reformulated Zagreb indices are defined respectively in terms of edge-degrees as EM₁(G)=∑_e∈Edeg(e)² and EM₂(G)=∑_e∼fdeg(e)deg(f), where deg(e) denotes the degree of the edge e, and e∼f means that the edges e and f are adjacent. We give upper and lower bounds for the first reformulated Zagreb index, and lower bounds for the second reformulated Zagreb index. Then we determine the extremal n-vertex unicyclic graphs with minimum and maximum first and second Zagreb indices, respectively. Furthermore, we introduce another generalization of Zagreb indices.     

Recognizing near-bipartite Pfaffian graphs in polynomial time

A matching covered graph is a non-trivial connected graph in which every edge is in some perfect matching. A non-bipartite matching covered graph G is near-bipartite if there are two edges e₁ and e₂ such that G−e₁−e₂ is bipartite and matching covered. In 2000, Fischer and Little characterized Pfaffian near-bipartite graphs in terms of forbidden subgraphs [I. Fischer, C.H.C. Little, A characterization of Pfaffian near bipartite graphs, J. Combin. Theory Ser. B 82 (2001) 175-222.]. However, their characterization does not imply a polynomial time algorithm to recognize near-bipartite Pfaffian graphs. In this article, we give such an algorithm.We define a more general class of matching covered graphs, which we call weakly near-bipartite graphs. This class includes the near-bipartite graphs. We give a polynomial algorithm for recognizing weakly near-bipartite Pfaffian graphs. We also show that Fischer and Little’s characterization of near-bipartite Pfaffian graphs extends to this wider class.     

Optimal graphs for chromatic polynomials

Let F(n,e) be the collection of all simple graphs with n vertices and e edges, and for G∈F(n,e) let P(G;λ) be the chromatic polynomial of G. A graph G∈F(n,e) is said to be optimal if another graph H∈F(n,e) does not exist with P(H;λ)?P(G;λ) for all λ, with strict inequality holding for some λ. In this paper we derive necessary conditions for bipartite graphs to be optimal, and show that, contrarily to the case of lower bounds, one can find values of n and e for which optimal graphs are not unique. We also derive necessary conditions for bipartite graphs to have the greatest number of cycles of length 4.     

An exact algorithm for MAX-CUT in sparse graphs

We study exact algorithms for the MAX-CUT problem. Introducing a new technique, we present an algorithmic scheme that computes a maximum cut in graphs with bounded maximum degree. Our algorithm runs in time O^*(2^(1-(2/Δ))n). We also describe a MAX-CUT algorithm for general graphs. Its time complexity is O^*(2^mn/(m+n)). Both algorithms use polynomial space.     

On vertex symmetric digraphs

The problem of how “near” we can come to a n-coloring of a given graph is investigated. I.e., what is the minimum possible number of edges joining equicolored vertices if we color the vertices of a given graph with n colors. In its generality the problem of finding such an optimal color assignment to the vertices (given the graph and the number of colors) is NP-complete. For each graph G, however, colors can be assigned to the vertices in such a way that the number of offending edges is less than the total number of edges divided by the number of colors. Furthermore, an Ω(epn) deterministic algorithm for finding such an n-color assignment is exhibited where e is the number of edges and p is the number of vertices of the graph (e?p?n). A priori solutions for the minimal number of offending edges are given for complete graphs; similarly for equicolored K_m in K_p and equicolored graphs in K_p.     

Edge-Distinguishing Index of a Graph

We introduce a concept of edge-distinguishing colourings of graphs. A closed neighbourhood of an edge \({e\in E(G)}\) is a subgraph N[e] induced by e and all edges adjacent to it. We say that a colouring c : E(G) → C does not distinguish two edges e ₁ and e ₂ if there exists an isomorphism φ of N[e ₁] onto N[e ₂] such that φ(e ₁) = e ₂ and φ preserves colours of c. An edge-distinguishing index of a graph G is the minimum number of colours in a proper colouring which distinguishes every two distinct edges of G. We determine the edge-distinguishing index for cycles, paths and complete graphs.     

Heavy cycles in k-connected weighted graphs with large weighted degree sums

A weighted graph is one in which every edge e is assigned a nonnegative number w(e), called the weight of e. The weight of a cycle is defined as the sum of the weights of its edges. The weighted degree of a vertex is the sum of the weights of the edges incident with it. In this paper, we prove that: Let G be a k-connected weighted graph with k?2. Then G contains either a Hamilton cycle or a cycle of weight at least 2m/(k+1), if G satisfies the following conditions: (1) The weighted degree sum of any k+1 pairwise nonadjacent vertices is at least m; (2) In each induced claw and each induced modified claw of G, all edges have the same weight. This generalizes an early result of Enomoto et al. on the existence of heavy cycles in k-connected weighted graphs.     

On the homogeneous algebraic graphs of large girth and their applications

Families of finite graphs of large girth were introduced in classical extremal graph theory. One important theoretical result here is the upper bound on the maximal size of the graph with girth ?2d established in Even Circuit Theorem by P. Erdös. We consider some results on such algebraic graphs over any field. The upper bound on the dimension of variety of edges for algebraic graphs of girth ?2d is established. Getting the lower bound, we use the family of bipartite graphs D(n,K) with n?2 over a field K, whose partition sets are two copies of the vector space Kⁿ. We consider the problem of constructing homogeneous algebraic graphs with a prescribed girth and formulate some problems motivated by classical extremal graph theory. Finally, we present a very short survey on applications of finite homogeneous algebraic graphs to coding theory and cryptography.     

Chromaticity of certain tripartite graphs identified with a path

For a graph G, let P(G) be its chromatic polynomial. Two graphs G and H are chromatically equivalent if P(G)=P(H). A graph G is chromatically unique if P(H)=P(G) implies that H≅G. In this paper, we classify the chromatic classes of graphs obtained from K_2,2,2∪P_m(m?3), (K_2,2,2-e)∪P_m(m?5) and (K_2,2,2-2e)∪P_m(m?6) by identifying the end-vertices of the path P_m with any two vertices of K_2,2,2, K_2,2,2-e and K_2,2,2-2e, respectively, where e and 2e are, respectively, an edge and any two edges of K_2,2,2. As a by-product of this, we obtain some families of chromatically unique and chromatically equivalent classes of graphs.     

On spanning tree congestion of graphs

Let G be a connected graph and T be a spanning tree of G. For e∈E(T), the congestion of e is the number of edges in G connecting two components of T−e. The edge congestion ofGinT is the maximum congestion over all edges in T. The spanning tree congestion ofG is the minimum congestion of G in its spanning trees. In this paper, we show the spanning tree congestion for the complete k-partite graphs and the two-dimensional tori. We also address lower bounds of spanning tree congestion for the multi-dimensional grids and the hypercubes.     

On the spectral radius of (0,1)-matrices

We determine the maximum spectral radius for (0,1)-matrices with k² andk²+1 1's, respectively, and for symmetric (0,1)-matrices with zero trace and $\text{e=}\text{k}\text{2}$1's (graphs with e edges). In all cases, equality is characterized.     

On a disparity between relative cliquewidth and relative NLC-width

Cliquewidth and NLC-width are two closely related parameters that measure the complexity of graphs. Both clique- and NLC-width are defined to be the minimum number of labels required to create a labelled graph by certain terms of operations. Many hard problems on graphs become solvable in polynomial-time if the inputs are restricted to graphs of bounded clique- or NLC-width. Cliquewidth and NLC-width differ at most by a factor of two.The relative counterparts of these parameters are defined to be the minimum number of labels necessary to create a graph while the tree-structure of the term is fixed. We show that Relative Cliquewidth and Relative NLC-width differ significantly in computational complexity. While the former problem is NP-complete the latter is solvable in polynomial time. The relative NLC-width can be computed in O(n³) time, which also yields an exact algorithm for computing the NLC-width in time O(3ⁿn). Additionally, our technique enables a combinatorial characterisation of NLC-width that avoids the usual operations on labelled graphs.     

On k-minimally n-edge-connected graphs

A graph is k-minimal with respect to some parameter if the removal of any j edges j<k reduces the value of that parameter by j. For k = 1 this concept is well-known; we consider multiple minimality, that is, k ? 2. We characterize all graphs which are multiply minimal with respect to connectivity or edge-connectivity. We also show that there are essentially no diagraphs which are multiply minimal with respect to diconnectivity or edge-diconnectivity. In addition, we investigate basic properties and multiple minimality for a variant of edge-connectivity which we call edge^m-connectivity.