A Planarity Test via Construction Sequences

Optimal linear-time algorithms for testing the planarity of a graph are well-known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. The approach is different from previous planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm runs in linear time and computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise

Radial level planarity with fixed embedding

We study level planarity testing of graphs with a fixed combinatorial embedding for three different notions of combinatorial embeddings, namely the level embedding, the upward embedding and the planar embedding. These notions allow for increasing degrees of freedom in their corresponding drawings. For the fixed level embedding there are known and easy to test level planarity criteria. We use these criteria to prove an "untangling" lemma that plays a key role in a simple level planarity test for the case where only the upward embedding is fixed. This test is then adapted to the case where only the planar embedding is fixed. Further, we characterize radial upward planar embeddings, which lets us extend our results to radial level planarity. No algorithms were previously known for these problems

A fast 0(n) Embedding Algorithm, based on the Hopcroft-Tarjan Planary Test

The embedding problem for a planar undirected graph G = (V,E) consists of constructing adjacency lists A(v) for each node v in V, in which all the neighbors of v appear in clockwise order with respect to a planar drawing of G. Such a set of adjacency lists is called a (combinatorial) embedding of G. Chiba presented a linear time algorithm based on the 'vertex-addition' planarity testing algorithm of Lempel, Even and Cederbaum using a PQ-tree. It is very complicated to implement this data structure. He also pointed out that it is fairly complicated to modify the linear 'path-addition' planarity testing algorithm of Hopcroft and Tarjan, such that it produces an embedding. We present a straightforward extension of the Hopcroft and Tarjan planarity testing algorithm which is easy to implement. Our method runs in linear time and performs very efficiently in practice

Planarity testing and embedding algorithms.

Thesis (M.Sc.)-University of Natal, Durban,1990.This thesis deals with several aspects of planar graphs, and some of the problems associated with non-planar graphs. Chapter 1 is devoted to introducing some of the fundamental notation and tools used in the remainder of the thesis. Graphs serve as useful models of electronic circuits. It is often of interest to know if a given electronic circuit has a layout on the plane so that no two wires cross. In Chapter 2, three efficient algorithms are described for determining whether a given 2-connected graph (which may model such a circuit) is planar. The first planarity testing algorithm uses a path addition approach. Although this algorithm is efficient, it does not have linear complexity. However, the second planarity testing algorithm has linear complexity, and uses a recursive fragment addition technique. The last planarity testing algorithm also has linear complexity, and relies on a relatively new data structure called PQ-trees which have several important applications to planar graphs. This algorithm uses a vertex addition technique. Chapter 3 further develops the idea of modelling an electronic circuit using a graph. Knowing that a given electronic circuit may be placed in the plane with no wires crossing is often insufficient. For example, some electronic circuits often have in excess of 100 000 nodes. Thus, obtaining a description of such a layout is important. In Chapter 3 we study two algorithms for obtaining such a description, both of which rely on the PQ-tree data structure. The first algorithm determines a rotational embedding of a 2-connected graph. Given a rotational embedding of a 2-connected graph, the second algorithm determines if a convex drawing of a graph is possible. If a convex drawing is possible, then we output the convex drawing. In Chapter 4, we concern ourselves with graphs that have failed a planarity test of Chapter 2. This is of particular importance, since complex electronic circuits often do not allow a layout on the plane. We study three different ways of approaching the problem of an electronic circuit modelled on a non-planar graph, all of which use the PQ-tree data structure. We study an algorithm for finding an upper bound on the thickness of a graph, an algorithm for determining the subgraphs of a non-planar graph which are subdivisions of the Kuratowski graphs K5 and K3,3, and lastly we present a new algorithm for finding an upper bound on the genus of a non-planar graph

Tr\'{e}maux trees and planarity

We present a simplified version of the DFS-based Left-Right planarity testing and embedding algorithm implemented in Pigale which has been considered as the fastest implemented one [J.M. Boyer, P.F. Cortese, M. Patrignani, and G. Di Battista. Stop minding your P's and Q's: implementing fast and simple DFS-based planarity and embedding algorithm. In Graph Drawing, volume 2912 of Lecture Notes in Computer Science, pages 25-36. Springer, 2004.]. We give here a simple full justification of the algorithm, based on a preliminary extended study of topological properties of DFS trees.Comment: Special Issue on Graph Drawin

A New Perspective on Clustered Planarity as a Combinatorial Embedding Problem

The clustered planarity problem (c-planarity) asks whether a hierarchically clustered graph admits a planar drawing such that the clusters can be nicely represented by regions. We introduce the cd-tree data structure and give a new characterization of c-planarity. It leads to efficient algorithms for c-planarity testing in the following cases. (i) Every cluster and every co-cluster (complement of a cluster) has at most two connected components. (ii) Every cluster has at most five outgoing edges. Moreover, the cd-tree reveals interesting connections between c-planarity and planarity with constraints on the order of edges around vertices. On one hand, this gives rise to a bunch of new open problems related to c-planarity, on the other hand it provides a new perspective on previous results.Comment: 17 pages, 2 figure