This paper deals with compact shortest path routing tables on weighted graphs with n nodes. For planar graphs we show how to construct in linear time shortest path routing tables that require 8n + o(n) bits per node, and O(log 2+ n) bit-operations per node to extract the route, for any constant > 0. We obtain the same bounds for graphs of crossing-edge number bounded by o(n= log n), and we generalize for graphs of genus bounded by > 0 yielding a size of n log +O(n) bits per node. Actually we prove a sharp upper bound of 2n log k +O(n) for graphs of pagenumber k, and a lower bound of n log k o(n log k) bits. These results are obtained by the use of dominating sets, compact coding of non-crossing partitions, and k-page representation of graphs.

vi Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii List of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv I Orthogonal Graph Drawing 1 1

) Abstract We consider the problem of maintaining the distances and the shortest paths from a single source in either a directed or an undirected graph with positive real edge weights, handling insertions, deletions and cost updates of edges. We propose fully dynamic algorithms with optimal space requirements and query time. The cost of update operations depends on the class of the considered graph and on the number of vertices that, due to an edge modification, either change their distance from the source or change their parent in the shortest path tree. In the case of graphs with bounded genus (including planar graphs), bounded degree graphs, bounded treewidth graphs and fi-near-planar graphs with bounded fi, the update procedures require O(log n) amortized time per vertex update, while for general graphs with n vertices and m edges they require O( p m log n) amortized time per vertex update. The solution is based on a dynamization of Dijkstra's algorithm [6] and requires simple ...

In this paper we propose the first experimental study of the fully dynamic single source shortest paths problem on directed graphs with positive real edge weights. In particular, we perform an experimental analysis of three different algorithms: Dijkstra's algorithm, and the two output bounded algorithms proposed by Ramalingam and Reps in [31] and by Frigioni, Marchetti-Spaccamela and Nanni in [18], respectively. The main goal of this paper is to provide a first experimental evidence for: (a) the effectiveness of dynamic algorithms for shortest paths with respect to a traditional static approach to this problem; (b) the validity of the theoretical model of output boundedness to analyze dynamic graph algorithms. Beside random generated graphs, useful to capture the "asymptotic" behavior of algorithms, we also develope experiments by considering a widely used graph from the real world, i.e., the Internet graph. Work partially supported by the ESPRIT Long Term Research Project...

Consider a drawing of a graph G in the plane such that crossing edges are coloured differently. The minimum number of colours, taken over all drawings of G, is the classical graph parameter thickness. By restricting the edges to be straight, we obtain the geometric thickness. By additionally restricting the vertices to be in convex position, we obtain the book thickness. This paper studies the relationship between these parameters and treewidth. Our first main result states that for graphs of treewidth k, the maximum thickness and the maximum geometric thickness both equal ⌈k/2⌉. This says that the lower bound for thickness can be matched by an upper bound, even in the more restrictive geometric setting. Our second main result states that for graphs of treewidth k, the maximum book thickness equals k if k ≤ 2 and equals k + 1 if k ≥ 3. This refutes a conjecture of Ganley and Heath [Discrete Appl. Math. 109(3):215–221, 2001]. Analogous results are proved for outerthickness, arboricity, and star-arboricity.

We give a survey of techniques for deriving lower bounds and algorithms for constructing upper bounds for several variations of the crossing number problem. Our aim is to emphasize the more general results or those results which have an algorithmic flavor, including the recent results of the authors. We also show applications of crossing numbers to other areas of discrete mathematics, like discrete geometry.

. We investigate book-thickness of subdivided complete and subdivided complete bipartite graphs. We discuss well-known results that the book-thickness of each of Kn and Kn;n is large when n is large, while, for every n, some subdivision of Kn and some subdivision of Kn;n have bookthickness at most three. The main result of this paper, whose proof is based on Ramsey theory, states that every graph obtained from Kn and Kn;n by subdividing each edge at most once has large book-thickness when n is large. Some generalizations of this result are also discussed. 1. Introduction Graph theory is a very youthful and vibrant part of mathematics. Many of its problems and results are readily accessible to a general audience. One of its particularly attractive areas, topological graph theory, deals with embedding graphs, viewed as topological spaces, into other topological spaces. In this paper, we will focus on embedding particular kinds of graphs: subdivisions of complete graphs and subdivisions ...

Let G = (V; E) be a t-partite graph with n vertices and m edges, where the partite sets are given. We present an O(n 2 m 1:5 ) time algorithm to construct drawings of G in the plane so that the size of the largest set of pairwise crossing edges, and at the same time, the size of the largest set of disjoint (pairwise non-crossing) edges are O( p t \Delta m). As an application we embed G in a book of O( p t \Delta m) pages, in O(n 2 m 1:5 ) time, resolving an open question for the pagenumber problem. A similar result is obtained for the dual of the pagenumber or the queuenumber. Our algorithms are obtained by derandomizing a probabilistic proof. 1 Introduction and Summary 1.1 Preliminaries Throughout this paper G = (V; E) is an undirected graph with jV j = n and jEj = m. A linear ordering of a set S is a bijection from S to f1; 2; : : : ; jSjg. Let h be a linear ordering of V . Consider a drawing of G that is obtained by placing the vertices along a straight line in the pl...

this paper we consider a variation of geometric thickness which lies between thickness and geometric thickness in which each edge has at most one bend. We are also interested in drawings with small area, which is an important consideration in VLSI and visualisation. To measure the area of a drawing we assume a vertex resolution rule; that is, pairs of vertices are at least unit-distance apart. A drawing obtained from a book embedding by positioning the vertices around a circle, as discussed above, has O(n ) area. The construction in [DEH00] demonstrating that (Kn ) d 4 e has O(n 6 ) area [D. Eppstein, personal communication ]. We prove the following 2-dimensional generalisation of the above-mentioned result in [Mal94b] for producing book embeddings

We introduce a generic approach for counting subgraphs in a graph. The main idea is to relate counting subgraphs to counting graph homomorphisms. This approach provides new algorithms and unifies several well known results in algorithms and combinatorics including the recent algorithm of Björklund, Husfeldt and Koivisto for computing the chromatic polynomial, the classical algorithm of Kohn, Gottlieb, Kohn, and Karp for counting Hamiltonian cycles, Ryser’s formula for counting perfect matchings of a bipartite graph, and color coding based algorithms of Alon, Yuster, and Zwick. By combining our method with known combinatorial bounds, ideas from succinct data structures, partition functions and the color coding technique, we obtain the following new results: • The number of optimal bandwidth permutations of a graph on n vertices excluding a fixed graph as a minor can be computed in time O(2 n+o(n)); in particular in time O(2 n n 3) for trees and in time 2 n+O( √ n) for planar graphs. • Counting all maximum planar subgraphs, subgraphs of bounded genus, or more generally