Block Graphs Research Articles

The diameter of sum basic equilibria games

We study the sum basic network creation game introduced in 2010 by Alon, Demaine, Hajiaghai and Leighton. In this game, an undirected and unweighted graph G is said to be a sum basic equilibrium if and only if, for every edge uv and any vertex v′ in G, swapping edge uv with edge uv′ does not decrease the total sum of the distances from u to all the other vertices. This concept lies at the heart of the network creation games, where the central problem is to understand the structure of the resulting equilibrium graphs, and in particular, how well they globally minimize the diameter. In this sense, in 2013 Alon et al. showed an upper bound of 2O(log⁡n) on the diameter of sum basic equilibria, and they also proved that if a sum basic equilibrium graph is a tree, then it has diameter at most 2. In this paper, we prove that the upper bound of 2 also holds for bipartite graphs and even for some non-bipartite classes like block graphs and cactus graphs.

On Three Domination-based Identification Problems in Block Graphs

The problems of determining the minimum-sized identifying, locating-dominating and open locating-dominating codes of an input graph are special search problems that are challenging from both theoretical and computational viewpoints. In these problems, one selects a dominating set C of a graph G such that the vertices of a chosen subset of V(G) (i.e. either V(G) \ C or V(G) itself) are uniquely determined by their neighborhoods in C. A typical line of attack for these problems is to determine tight bounds for the minimum codes in various graph classes. In this work, we present tight lower and upper bounds for all three types of codes for block graphs (i.e. diamond-free chordal graphs). Our bounds are in terms of the number of maximal cliques (or blocks) of a block graph and the order of the graph. Two of our upper bounds verify conjectures from the literature with one of them being now proven for block graphs in this article. As for the lower bounds, we prove them to be linear in terms of both the number of blocks and the order of the block graph. We provide examples of families of block graphs whose minimum codes attain these bounds, thus showing each bound to be tight.

Non-canonical maximum cliques without a design structure in the block graphs of 2-designs

Non-canonical maximum cliques without a design structure in the block graphs of 2-designs

Computing the Heaviest Conflict-free Sub-DAG in DAG-based DLTs

In this paper, we consider DAG-based Distributed Ledger Technologies (DLTs), i.e. , DLTs where each block can reference several previous blocks hence forming a Directed Acyclic Graph of Blocks (BDAG). Each block has a weight (usually a constant normalized to one) and our goal is to compute the heaviest sub-BDAG that does not contain conflicting blocks. First, we prove that computing such a sub-BDAG is NP-complete. Then, we show that the difficulty comes from concurrent conflicts and we present an optimal algorithm that is polynomial if the number of concurrent conflicts is bounded. We also give an efficient incremental version of our algorithm. Finally, we evaluate the performance of our algorithm on random BDAGs against an existing algorithm called GHOSTDAG and show that, in addition to being optimal, our algorithm is also more efficient in practice.

On maximal Roman domination in graphs: complexity and algorithms

For a simple undirected connected graph G = (V, E), a maximal Roman dominating function (MRDF) of G is a function f : V (G) → {0, 1, 2} with the following properties: (i) For every vertex v ∈ {v ∈ V|f(v) = 0}, there exists a vertex u ∈ N(v) such that f(u) = 2. (ii) The set {v ∈ V|f(v) = 0} is not a dominating set of G; In other words, there exists a vertex v ∈ {v ∈ V|f(v) ≠ 0} such that N(v) ∩ {u ∈ V|f(u) = 0} ∅. The weight of an MRDF of G is the sum of its function values over all vertices, denoted as f(G) = ∑v∈V (G) f(v), and the maximal Roman domination number of G, denoted by γmR(G), is the minimum weight of an MRDF of G. In this paper, we establish some bounds of the maximal Roman domination number of graphs. Additionally, we develop an integer linear programming formulation to compute the maximal Roman domination number of any graph. Furthermore, we prove that maximal Roman domination problem (MRD) is NP-complete even restricted to star convex bipartite graphs and chordal bipartite graphs. Lastly, we show the maximal Roman domination number of threshold graphs, trees, and block graphs can be computed in linear time.

On the general position number of the k-th power graphs

A set is called a general position set of a graph G if any triple set V 0 of R is non-geodesic, that is, three elements of V 0 cannot lie on the same geodesic of G. The general position number gp(G) of a graph G is the cardinality of a largest general position set of G. The k-th power, Gk , of a graph G is obtained from G by adding a new edge between each pair of vertices with a distance of at most k in G. In this paper we establish the bounds for gp(Gk ) and and give an explicit formula of . Also we consider the relation between gp(G 2) and gp(G). We deduce a formula of and provide the upper bound on gp(G 2)−gp(G) for general block graphs G. Moreover, we determine the gp-number for the square of trees.

On the (Non-)Existence of Tight Distance-Regular Graphs: a Local Approach

Let $\Gamma$ denote a distance-regular graph with diameter $D\geq 3.$ Jurišić and Vidali conjectured that if $\Gamma$ is tight with classical parameters $(D,b,\alpha,\beta)$, $b\geq 2$, then $\Gamma$ is not locally the block graph of an orthogonal array nor the block graph of a Steiner system. In the present paper, we prove this conjecture and, furthermore, extend it from the following aspect. Assume that for every triple of vertices $x, y, z$ of $\Gamma$, where $x$ and $y$ are adjacent, and $z$ is at distance $2$ from both $x$ and $y$, the number of common neighbors of $x$, $y$, $z$ is constant. We then show that if $\Gamma$ is locally the block graph of an orthogonal array (resp.~a Steiner system) with smallest eigenvalue $-m$, $m\geq 3$, then the intersection number $c_2$ is not equal to $m^2$ (resp. $m(m+1)$). Using this result, we prove that if a tight distance-regular graph $\Gamma$ is not locally the block graph of an orthogonal array or a Steiner system, then the valency (and hence diameter) of $\Gamma$ is bounded by a function in the parameter $b=b_1/(1+\theta_1)$, where $b_1$ is the intersection number of $\Gamma$ and $\theta_1$ is the second largest eigenvalue of $\Gamma$.

The Graft Transformation and Their Application on the Spectral Radius of Block Graphs

Let [Formula: see text] be a connected graph and [Formula: see text] be the adjacency matrix of [Formula: see text]. Suppose that [Formula: see text] are the eigenvalues of [Formula: see text]. In this paper, we first give a graft transformation on the spectral radius of graphs and then as their application, we determine the extremal graphs with maximum and minimum spectral radii among all clique trees. Furthermore, we also determine the unique graph with maximum spectral radius among all block graphs by using different methods.

Connected greedy colourings of perfect graphs and other classes: the good, the bad and the ugly

The Grundy number of a graph is the maximum number of colours used by the "First-Fit" greedy colouring algorithm over all vertex orderings. Given a vertex ordering $\sigma= v_1,\dots,v_n$, the "First-Fit" greedy colouring algorithm colours the vertices in the order of $\sigma$ by assigning to each vertex the smallest colour unused in its neighbourhood. By restricting this procedure to vertex orderings that are connected, we obtain {\em connected greedy colourings}. For some graphs, all connected greedy colourings use exactly $\chi(G)$ colours; they are called {\em good graphs}. On the opposite, some graphs do not admit any connected greedy colouring using only $\chi(G)$ colours; they are called {\em ugly graphs}. We show that no perfect graph is ugly. We also give simple proofs of this fact for subclasses of perfect graphs (block graphs, comparability graphs), and show that no $K_4$-minor free graph is ugly. Moreover, our proofs are constructive, and imply the existence of polynomial-time algorithms to compute good connected orderings for these graph classes.

Extremal Edge General Position Sets in Some Graphs

A set of edges X⊆E(G)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$X\\subseteq E(G)$$\\end{document} of a graph G is an edge general position set if no three edges from X lie on a common shortest path. The edge general position number gpe(G)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extrm{gp}}_{\ extrm{e}}(G)$$\\end{document} of G is the cardinality of a largest edge general position set in G. Graphs G with gpe(G)=|E(G)|-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extrm{gp}}_{{\ extrm{e}}}(G) = |E(G)| - 1$$\\end{document} and with gpe(G)=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extrm{gp}}_{{\ extrm{e}}}(G) = 3$$\\end{document} are respectively characterized. Sharp upper and lower bounds on gpe(G)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extrm{gp}}_{{\ extrm{e}}}(G)$$\\end{document} are proved for block graphs G and exact values are determined for several specific block graphs.

Smaller kernels for two vertex deletion problems

In this paper we consider two vertex deletion problems. In the Block Vertex Deletion problem, the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k vertices whose removal from G result in a block graph (a graph in which every biconnected component is a clique). In the Pathwidth One Vertex Deletion problem, the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k vertices whose removal from G result in a graph with pathwidth at most one. We give a kernel for Block Vertex Deletion with O(k3) vertices and a kernel for Pathwidth One Vertex Deletion with O(k2) vertices. Our results improve the previous O(k4)-vertex kernel for Block Vertex Deletion (Agrawal et al., 2016 [1]) and the O(k3)-vertex kernel for Pathwidth One Vertex Deletion (Cygan et al., 2012 [3]).

Approximation algorithms for job scheduling with block-type conflict graphs

The problem of scheduling jobs on parallel machines (identical, uniform, or unrelated), under incompatibility relation modeled as a block graph, under the makespan optimality criterion, is considered in this paper. No two jobs that are in the relation (equivalently in the same block) may be scheduled on the same machine in this model.The presented model stems from a well-established line of research combining scheduling theory with methods relevant to graph coloring. Recently, cluster graphs and their extensions like block graphs were given additional attention. We complement hardness results provided by other researchers for block graphs by providing approximation algorithms. In particular, we provide a 2-approximation algorithm for P|G=block graph|Cmax and a PTAS for the case when the jobs are unit time in addition. In the case of uniform machines, we analyze two cases. The first one is when the number of blocks is bounded, i.e. Q|G=k − block graph|Cmax. For this case, we provide a PTAS, improving upon results presented by D. Page and R. Solis-Oba. The improvement is two-fold: we allow richer graph structure, and we allow the number of machine speeds to be part of the input. Due to strong NP-hardness of Q|G=2 − clique graph|Cmax, the result establishes the approximation status of Q|G=k − block graph|Cmax. The PTAS might be of independent interest because the problem is tightly related to the NUMERICAL k-DIMENSIONAL MATCHING WITH TARGET SUMS problem. The second case that we analyze is when the number of blocks is arbitrary, but the number of cut-vertices is bounded and jobs are of unit time. In this case, we present an exact algorithm. In addition, we present an FPTAS for graphs with bounded treewidth and a bounded number of unrelated machines.The paper ends with extensive tests of the selected algorithms.

The median function of a block graph: Axiomatic characterizations

A profile π=(x1,x2,…,xk) of length k in a connected graph G is a sequence of vertices in G with possible repetitions of vertices. A median x of a profile π in G is a vertex that minimizes the remoteness value, that is, the sum of the distances from x to the elements xi in π is minimized. The median function output the set of medians (denoted as Med(π)) of π, for every profile π in G. It is one of the basic models for the location of a desirable facility in a network. The median function on graphs is well studied. For instance, it has been characterized axiomatically by simple axioms on trees, hypercubes, median graphs, cocktail-party graphs, and complete graphs minus a matching. In this paper, we study the median sets and median function of block graphs, an immediate generalization of trees. We determine the median sets of all types of profiles and we obtain two sets of axiomatic characterizations of the median function on block graphs.

Euclidean and circum-Euclidean distance matrices: characterizations and interlacing property

Motivated by the inverse formula of the distance matrix of a tree and the Moore–Penrose inverse of a circum-Euclidean distance matrix (CEDM), in this paper, we study a general real square matrix M whose Moore–Penrose inverse can be expressed as the sum of a Laplacian-like matrix L and a rank one matrix. In particular, for a symmetric hollow matrix M, under an assumption, we show that M is a Euclidean distance matrix if and only if L is positive semidefinite. Based on this, we obtain a new characterization for CEDMs involving their Moore–Penrose inverses. As an application, we show that the distance matrices of block graphs and odd-cycle-clique graphs are CEDMs. Finally, we establish an interlacing property between the eigenvalues of a Euclidean distance matrix M (including the singular case) and its associated Laplacian-like matrix L, which generalizes the interlacing property proved for the distance matrices of trees.

Changes in the physiognomy of the archaeological landscape - learning from the past to build the future (with prehistorical burial sites in Poland as an example)

The aims of this research were to identify the key processes that have occurred in the past that have caused changes in the physiognomy of archaeological landscape and to indicate possible future processes, along with their landscape implications. The study was based on cartographic and literature studies, and field visits. It covered an analysis of the land cover, the history of archaeological research, the establishment of forms of legal protection and tourist infrastructure development. The past changes are visualised for each site in theform of a block graph. Possible future scenarios with landscape implications are presented on a tree diagram.

Sharp Lower Bound of Cacti Graph with respect to Zagreb Eccentricity Indices

The first Zagreb eccentricity index E1(℧) is the sum of square of eccentricities of the vertices, and the second Zagreb eccentricity index E2(℧) is the sum of product squares of the eccentricities of the vertices. A linked graph G is called a cactus if any two of its cycles share only one vertex. In other words, there are no two independent cycles that share an edge. Cactus graphs are also known as “block graphs” or “sensitized graphs.” They are closely related to chordal graphs and can be used to represent various types of networks, including communication networks and road networks. In this contribution, E1(℧) and E2(℧) values of cacti with k pendant vertices and k cycles, respectively, are considered. We determine the minimum E1, E2 indices for n order cacti with k pendant vertices and k cycles.

Relating the annihilation number and the 2-domination number for unicyclic graphs

The 2-domination number ?2(G) of a graph G is the minimum cardinality of a set S ? V(G) such that every vertex from V(G)\S is adjacent to at least two vertices in S. The annihilation number a(G) is the largest integer k such that the sum of the first k terms of the non-decreasing degree sequence of G is at most the number of its edges. It was conjectured that ?2(G) ? a(G)+1 holds for every non-trivial connected graph G. The conjecture was earlier confirmed for graphs of minimum degree 3, trees, block graphs and some bipartite cacti. However, a class of cacti were found as counterexample graphs recently by Yue et al. [9] to the above conjecture. In this paper, we consider the above conjecture from the positive side and prove that this conjecture holds for all unicyclic graphs.

Some Connectivity Parameters of Interval-Valued Intuitionistic Fuzzy Graphs with Applications

Connectivity in graphs is useful in describing different types of communication systems like neural networks, computer networks, etc. In the design of any network, it is essential to evaluate the connections based on their strengths. In this manuscript, we comprehensively describe various connectivity parameters related to interval-valued intuitionistic fuzzy graphs (IVIFGs). These are the generalizations of the parameters defined for fuzzy graphs, interval-valued fuzzy graphs, and intuitionistic fuzzy graphs. First, we introduce interval-valued intuitionistic fuzzy bridges (IVIF bridges) and interval-valued intuitionistic fuzzy cut-nodes (IVIF cut-nodes). We discuss the many characteristics of these terms as well as establish the necessary and sufficient conditions for an arc to become an IVIF-bridge and a vertex to be an IVIF-cutnode. Furthermore, we initiate the concepts of interval-valued intuitionistic fuzzy cycles (IVIFCs) and interval-valued intuitionistic fuzzy trees (IVIFTs) and explore few relationships among them. In addition, we introduce the notions of fuzzy blocks and fuzzy block graphs and extend these terms as interval-valued fuzzy blocks (IVF-blocks) and interval-valued intuitionistic fuzzy block graphs (IVIF-block graphs). Finally, we provide the application of interval-valued intuitionistic fuzzy trees (IVIFTs) in a road transport network.

Maximization of the spectral radius of block graphs with a given dissociation number

A connected graph is called a block graph if each of its blocks is a complete graph. Let Bl(k,φ) be the class of block graphs on k vertices with given dissociation number φ. In this article, we have shown the existence and uniqueness of a block graph Bk,φ in Bl(k,φ) that maximizes the spectral radius ρ(G) among all graphs G in Bl(k,φ). Furthermore, we also provide bounds on ρ(Bk,φ).

The extremal spectral radius of generalized block graphs

A block graph is a graph in which every block is a complete graph. Denote by Kn,q the set of block graphs with n vertices and all blocks on q+1 vertices for every q≥2. Recently, Zhao and Liu (2023) determined the minimum spectral radius of graphs in Kn,q, which verified a conjecture posed by Conde et al. (2022). Replacing the complete graph by a general block or a cycle, we define a generalized block graph or a cycle tree, respectively. Let Bn,q (resp. Cn,q) be the set of generalized block graphs (resp. cycle trees) on n vertices and in which each block is of order q+1 for q≥2. In this paper, we obtain the maximum/minimum spectral radius of a graph in Cn,q. Furthermore, we determine the extremal graph attaining the minimum spectral radius among graphs in Bn,q, which coincides with that in Cn,q.