Localized and compact data-structure for comparability graphs

We show that every comparability graph of any two-dimensional poset over n elements (a.k.a. permutation graph) can be preprocessed in O(n) time, if two linear extensions of the poset are given, to produce an O(n) space data-structure supporting distance queries in constant time. The data-structure is localized and given as a distance labeling, that is each vertex receives a label of O(logn) bits so that distance queries between any two vertices are answered by inspecting their labels only. This result improves the previous scheme due to Katz, Katz and Peleg [M. Katz, N.A. Katz, D. Peleg, Distance labeling schemes for well-separated graph classes, Discrete Applied Mathematics 145 (2005) 384-402] by a log n factor.As a byproduct, our data-structure supports all-pair shortest-path queries in O(d) time for distance-d pairs, and so identifies in constant time the first edge along a shortest path between any source and destination.More fundamentally, we show that this optimal space and time data-structure cannot be extended for higher dimension posets. More precisely, we prove that for comparability graphs of three-dimensional posets, every distance labeling scheme requires Ω(n^1/3) bit labels.     

The clique-separator graph for chordal graphs

We present a new representation of a chordal graph called the clique-separator graph, whose nodes are the maximal cliques and minimal vertex separators of the graph. We present structural properties of the clique-separator graph and additional properties when the chordal graph is an interval graph, proper interval graph, or split graph. We also characterize proper interval graphs and split graphs in terms of the clique-separator graph. We present an algorithm that constructs the clique-separator graph of a chordal graph in O(n³) time and of an interval graph in O(n²) time, where n is the number of vertices in the graph.     

An implicit representation of chordal comparability graphs in linear time

Ma and Spinrad have shown that every transitive orientation of a chordal comparability graph is the intersection of four linear orders. That is, chordal comparability graphs are comparability graphs of posets of dimension four. Among other uses, this gives an implicit representation of a chordal comparability graph using O(n) integers so that, given two vertices, it can be determined in O(1) time whether they are adjacent, no matter how dense the graph is. We give a linear time algorithm for finding the four linear orders, improving on their bound of O(n²).     

A trust region method for solving the decentralized static output feedback design problem

The shortest-paths problem is a fundamental problem in graph theory and finds diverse applications in various fields. This is why shortest path algorithms have been designed more thoroughly than any other algorithm in graph theory. A large number of optimization problems are mathematically equivalent to the problem of finding shortest paths in a graph. The shortest-path between a pair of vertices is defined as the path with shortest length between the pair of vertices. The shortest path from one vertex to another often gives the best way to route a message between the vertices. This paper presents anO(n ²) time sequential algorithm and anO(n ²/p+logn) time parallel algorithm on EREW PRAM model for solving all pairs shortest paths problem on circular-arc graphs, wherep andn represent respectively the number of processors and the number of vertices of the circular-arc graph.     

Fast parallel graph searching with applications

This paper presents fast parallel algorithms for the following graph theoretic problems: breadth-depth search of directed acyclic graphs; minimum-depth search of graphs; finding the minimum-weighted paths between all node-pairs of a weighted graph and the critical activities of an activity-on-edge network. The first algorithm hasO(logdlogn) time complexity withO(n ³) processors and the remaining algorithms achieveO(logd loglogn) time bound withO(n ²[n/loglogn]) processors, whered is the diameter of the graph or the directed acyclic graph (which also represents an activity-on-edge network) withn nodes. These algorithms work on an unbounded shared memory model of the single instruction stream, multiple data stream computer that allows both read and write conflicts.     

Characterizations for co-graphs defined by restricted NLC-width or clique-width operations

In this paper we characterize subclasses of co-graphs defined by restricted NLC-width operations and subclasses of co-graphs defined by restricted clique-width operations.We show that a graph has NLCT-width 1 if and only if it is (C₄,P₄)-free. Since (C₄,P₄)-free graphs are exactly trivially perfect graphs, the set of graphs of NLCT-width 1 is equal to the set of trivially perfect graphs, and a recursive definition for trivially perfect graphs follows. Further we show that a graph has linear NLC-width 1 if and only if is (C₄,P₄,2K₂)-free. This implies that the set of graphs of linear NLC-width 1 is equal to the set of threshold graphs.We also give forbidden induced subgraph characterizations for co-graphs defined by restricted clique-width operations using P₄, 2K₂, and co-2P₃.     

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.     

Distance and routing labeling schemes for non-positively curved plane graphs

Distance labeling schemes are schemes that label the vertices of a graph with short labels in such a way that the distance between any two vertices u and v can be determined efficiently (e.g., in constant or logarithmic time) by merely inspecting the labels of u and v, without using any other information. Similarly, routing labeling schemes are schemes that label the vertices of a graph with short labels in such a way that given the label of a source vertex and the label of a destination, it is possible to compute efficiently (e.g., in constant or logarithmic time) the port number of the edge from the source that heads in the direction of the destination. In this paper we show that the three major classes of non-positively curved plane graphs enjoy such distance and routing labeling schemes using O(log²n) bit labels on n-vertex graphs. In constructing these labeling schemes interesting metric properties of those graphs are employed.     

On the complexity of computing treelength

We resolve the computational complexity of determining the treelength of a graph, thereby solving an open problem of Dourisboure and Gavoille, who introduced this parameter, and asked to determine the complexity of recognizing graphs of a bounded treelength Dourisboure and Gavoille (2007) [6]. While recognizing graphs with treelength 1 is easily seen as equivalent to recognizing chordal graphs, which can be done in linear time, the computational complexity of recognizing graphs with treelength 2 was unknown until this result. We show that the problem of determining whether a given graph has a treelength at most k is NP-complete for every fixed k≥2, and use this result to show that treelength in weighted graphs is hard to approximate within a factor smaller than . Additionally, we show that treelength can be computed in time O^∗(1.7549ⁿ) by giving an exact exponential time algorithm for the Chordal Sandwich problem and showing how this algorithm can be used to compute the treelength of a graph.     

A Faster Katz Status Score Algorithm

A new graph theoretical algorithm to calculate Katz status scores reduces computational complexity from time O(n ³) to O(n + m). Randomly-generated graphs as well as data from a large empiric study are used to test the performance of two commercial network analysis packages (GRADAP and UCINET V), compared to the performance achieved by the authors' algorithm, implemented in Visual Basic.     

On computing the distinguishing and distinguishing chromatic numbers of interval graphs and other results

A vertex k-coloring of graph G is distinguishing if the only automorphism of G that preserves the colors is the identity map. It is proper-distinguishing if the coloring is both proper and distinguishing. The distinguishing number ofG, D(G), is the smallest integer k so that G has a distinguishing k-coloring; the distinguishing chromatic number ofG, χ_D(G), is defined similarly.It has been shown recently that the distinguishing number of a planar graph can be determined efficiently by counting a related parameter-the number of inequivalent distinguishing colorings of the graph. In this paper, we demonstrate that the same technique can be used to compute the distinguishing number and the distinguishing chromatic number of an interval graph. We make use of PQ-trees, a classic data structure that has been used to recognize and test the isomorphism of interval graphs; our algorithms run in O(n³log³n) time for graphs with n vertices. We also prove a number of results regarding the computational complexity of determining a graph’s distinguishing chromatic number.     

On powers of graphs of bounded NLC-width (clique-width)

Given a graph G, the graph G^l has the same vertex set and two vertices are adjacent in G^l if and only if they are at distance at most l in G. The l-coloring problem consists in finding an optimal vertex coloring of the graph G^l, where G is the input graph. We show that, for any fixed value of l, the l-coloring problem is polynomial when restricted to graphs of bounded NLC-width (or clique-width), if an expression of the graph is also part of the input. We also prove that the NLC-width of G^l is at most 2(l+1)^nlcw(G).     

Proximity‐preserving labeling schemes

This article considers informative labeling schemes for graphs. Specifically, the question introduced is whether it is possible to label the vertices of a graph with short labels in such a way that the distance between any two vertices can be inferred from inspecting their labels. A labeling scheme enjoying this property is termed a proximity‐preserving labeling scheme. It is shown that, for the class of n‐vertex weighted trees with M‐bit edge weights, there exists such a proximity‐preserving labeling scheme using O(M log n + log²n) bit labels. For the family of all n‐vertex unweighted graphs, a labeling scheme is proposed that using O(log² n · κ · n^1/κ) bit labels can provide approximate estimates to the distance, which are accurate up to a factor of In particular, using O(log³n) bit labels, the scheme can provide estimates accurate up to a factor of $\sqrt{2 \log n}$. (For weighted graphs, one of the log n factors in the label size is replaced by a factor logarithmic in the network's diameter.) © 2000 John Wiley & Sons, Inc. J Graph Theory 33: 167–176, 2000     

A divide-and-conquer algorithm for constructing relative neighborhood graph

AnO(n logn) divide-and-conquer algorithm for finding the relative neighborhood graph RNG(V) of a set V ofn points in Euclidean space is presented. If implemented in parallel, its time complexity isO(n) and it requiresO(logn) processors.     

Graph bisection algorithms with good average case behavior

In the paper, we describe a polynomial time algorithm that, for every input graph, either outputs the minimum bisection of the graph or halts without output. More importantly, we show that the algorithm chooses the former course with high probability for many natural classes of graphs. In particular, for every fixedd≧3, all sufficiently largen and allb=o(n ^{1?1/[(d+1)/2]}), the algorithm finds the minimum bisection for almost alld-regular labelled simple graphs with 2n nodes and bisection widthb. For example, the algorithm succeeds for almost all 5-regular graphs with 2n nodes and bisection widtho(n ^2/3). The algorithm differs from other graph bisection heuristics (as well as from many heuristics for other NP-complete problems) in several respects. Most notably:

1. the algorithm provides exactly the minimum bisection for almost all input graphs with the specified form, instead of only an approximation of the minimum bisection,
2. whenever the algorithm produces a bisection, it is guaranteed to be optimal (i.e., the algorithm also produces a proof that the bisection it outputs is an optimal bisection),
3. the algorithm works well both theoretically and experimentally,
4. the algorithm employs global methods such as network flow instead of local operations such as 2-changes, and
5. the algorithm works well for graphs with small bisections (as opposed to graphs with large bisections, for which arbitrary bisections are nearly optimal).

     

Dynamic programming and planarity: Improved tree-decomposition based algorithms

We study some structural properties for tree-decompositions of 2-connected planar graphs that we use to improve upon the runtime of tree-decomposition based dynamic programming approaches for several NP-hard planar graph problems. E.g., we derive the fastest algorithm for Planar Dominating Set of runtime 3^tw⋅n^O(1), when we take the width tw of a given tree-decomposition as the measure for the exponential worst case behavior. We also introduce a tree-decomposition based approach to solve non-local problems efficiently, such as Planar Hamiltonian Cycle in runtime 6^tw⋅n^O(1). From any input tree-decomposition of a 2-connected planar graph, one computes in time O(nm) a tree-decomposition with geometric properties, which decomposes the plane into disks, and where the graph separators form Jordan curves in the plane.     

On generalized Petersen graphs labeled with a condition at distance two

An L(2,1)-labeling of graph G is an integer labeling of the vertices in V(G) such that adjacent vertices receive labels which differ by at least two, and vertices which are distance two apart receive labels which differ by at least one. The λ-number of G is the minimum span taken over all L(2,1)-labelings of G. In this paper, we consider the λ-numbers of generalized Petersen graphs. By introducing the notion of a matched sum of graphs, we show that the λ-number of every generalized Petersen graph is bounded from above by 9. We then show that this bound can be improved to 8 for all generalized Petersen graphs with vertex order >12, and, with the exception of the Petersen graph itself, improved to 7 otherwise.     

A non-factorial algorithm for canonical numbering of a graph

A graph certificate or canonical form is a short unique (up to isomorphism) representation of the graph. Thus two graphs are isomorphic iff their certificates are identical. In this paper an O(cⁿ) graph isomorphism algorithm which also yields a certificate of the graph is presented. The certificate produced by this algorithm is a canonical numbering of the vertices of the graph.     

A local characterization of bounded clique-width for line graphs

It is shown that a line graph G has clique-width at most 8k+4 and NLC-width at most 4k+3, if G contains a vertex whose non-neighbours induce a subgraph of clique-width k or NLC-width k in G, respectively. This relation implies that co-gem-free line graphs have clique-width at most 14 and NLC-width at most 7.It is also shown that in a line graph the neighbours of a vertex induce a subgraph of clique-width at most 4 and NLC-width at most 2.     

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.