EXPLORING PLANAR GRAPHS USING UNORIENTED MAPS

We study the amount of knowledge about the network that is required in order to efficiently solve a task concerning this network. The impact of available information on the efficiency of solving network problems, such as communication or exploration, has been investigated before but assumptions concerned availability of particular items of information about the network, such as the size, the diameter, or a map of the network. In contrast, our approach is quantitative: we investigate the minimum number of bits of information (minimum oracle size) that has to be given to an algorithm in order to perform a task with given efficiency. We illustrate this quantitative approach to available knowledge by the task of tree exploration. A mobile entity (robot) has to traverse all edges of an unknown tree, using as few edge traversals as possible. The quality of an exploration algorithm ${\cal A}$ is measured by its competitive ratio, i.e., by comparing its cost (number of edge traversals) to the length of the shortest path containing all edges of the tree. Depth-First-Search has competitive ratio 2 and, in the absence of any information about the tree, no algorithm can beat this value. We determine the minimum number of bits of information that has to be given to an exploration algorithm in order to achieve competitive ratio strictly smaller than 2. Our main result establishes an exact threshold oracle size that turns out to be roughly loglogD, where D is the diameter of the tree. More precisely, for any constant c, we construct an exploration algorithm with competitive ratio smaller than 2, using an oracle of size at most loglogD –c, and we show that every algorithm using an oracle of size loglogD –g(D), for any function g unbounded from above, has competitive ratio at least 2.

Tree exploration with advice

When we want to draw a graph to make the information contained in its structure easily accessible, it is highly desirable to have a drawing with as few edge crossings as possible (Purchase et al., 1997; Purchase, 1997). The class of graphs that can be drawn with no crossings at all is the class of planar graphs. Algorithms for drawing planar graphs are the main subject of this chapter.

A dominating set $$D \subseteq V$$D⊆V in a graph $$G=(V,E)$$G=(V,E) is a subset of vertices such that every vertex in V belongs to D or has at least one neighbour in D. In this paper we deal with the problem of finding an approximation of the dominating set of minimum size, i.e., the approximation of the minimum dominating set problem (MDS) in a distributed setting. A distributed algorithm that runs in a constant number of rounds, independent of the size of the network, is called local. In research on distributed local algorithms it is commonly assumed that each vertex has an unique identifier. However, as was shown by Goos et al., for certain classes of graphs (for example, lift-closed bounded degree graphs) identifiers are unnecessary and only a port numbering is needed. We confirm that the same remains true for the MDS up to a constant factor in the class of planar graphs. Namely, we present a local deterministic 694-approximation algorithm for the MDS in planar graphs in a model with a port numbering only. Moreover, our algorithm uses only short messages, i.e., in each round each node can send only a $$O(\log {|V|})$$O(log|V|)-bit message to each of its neighbours.

In this paper we study the planar graphs that admit an acyclic 3-coloring. We show that testing acyclic 3-colorability is $\cal NP$-hard for planar graphs of maximum degree 4 and we show that there exist infinite classes of cubic planar graphs that are not acyclically 3-colorable. Further, we show that every planar graph has a subdivision with one vertex per edge that is acyclically 3-colorable. Finally, we characterize the series-parallel graphs such that every 3-coloring is acyclic and we provide a linear-time recognition algorithm for such graphs.

Homomorphisms of partial t-trees and edge-colorings of partial 3-trees

On external-memory MST, SSSP and multi-way planar graph separation

Finding Paths between graph colourings: PSPACE-completeness and superpolynomial distances

Even though a large number of I/O-efficient graph algorithms have been developed, a number of fundamental problems still remain open. For example, no space- and I/O-efficient algorithms are known for depth-first search or breadth-first search in sparse graphs. In this paper we present two new results on I/O-efficient depth-first search in an important class of sparse graphs, namely undirected embedded planar graphs. We develop a new efficient depth-first search algorithm and show how planar depth-first search in general can be reduced to planar breadth-first search. As part of the first result we develop the first I/O-efficient algorithm for finding a simple cycle separator of a biconnected planar graph. Together with other recent reducibility results, the second result provides further evidence that external memory breadth-first search is among the hardest problems on planar graphs.

Even though a large number of I/O-efficient graph algorithms have been developed, a number of fundamental problems still remain open. For example, no space- and I/O-efficient algorithms are known for depth-first search or breadth-first search in sparse graphs. In this paper we present two new results on I/O-efficient depth-first search in an important class of sparse graphs, namely undirected embedded planar graphs. We develop a new efficient depth-first search algorithm and show how planar depth-first search in general can be reduced to planar breadth-first search. As part of the first result we develop the first I/O-efficient algorithm for finding a simple cycle separator of a biconnected planar graph. Together with other recent reducibility results, the second result provides further evidence that external memory breadth-firstsearch is among the hardest problems on planar graphs.KeywordsPlanar GraphExternal MemorySimple CycleOuterplanar GraphBoundary CycleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An embedding of a graph in a book consists of a linear order of its vertices along the spine of the book and of an assignment of its edges to the pages of the book, so that no two edges on the same page cross. The book thickness of a graph is the minimum number of pages over all its book embeddings. Accordingly, the book thickness of a class of graphs is the maximum book thickness over all its members. In this paper, we address a long-standing open problem regarding the exact book thickness of the class of planar graphs, which previously was known to be either three or four. We settle this problem by constructing planar graphs that require four pages in all of their book embeddings, thus establishing that the book thickness of the class of planar graphs is four.

Edge Separators of Planar and Outerplanar Graphs With Applications

Greenʼs theorem and isolation in planar graphs

We consider the degree-preserving spanning tree (DPST) problem: Given a connected graph G, find a spanning tree T of G such that as many vertices of T as possible have the same degree in T as in G. This problem is a graph-theoretical translation of a problem arising in the system-theoretical context of identifiability in networks, a concept which has applications in, for example, water distribution networks and electrical networks. We show that the DPST problem is NP-complete, even when restricted to split graphs or bipartite planar graphs, but that it can be solved in polynomial time for graphs with a bounded asteroidal number and for graphs with a bounded treewidth. For the class of interval graphs, we give a linear time algorithm. For the class of cocomparability graphs, we give an O(n4) algorithm. Furthermore, we present linear time approximation algorithms for planar graphs of a worst-case performance ratio of 1 - e for every e > 0.

This paper deals with the problem of mission allocation for robot agents cooperating with human agents, in industrial application. RFID (Radio frequency identification) sensor platform is exploited to describe the environment state. Relevant information is available in RFID tags which are placed on objects and machines, and agents (robots or humans) types. The mission allocator receives the information from the RFID platform, and calculates the adequate sub-mission (set of sequential tasks) assigned to each agent. The robot agents execute the assigned tasks, organized through task planning strategy. The human agents perform the assigned sub-mission plans introduced via Human Machine Interfaces (HMIs). After the execution of each task by the agents, the environment state is updated. A general framework allowing mission distribution for robot and human agents is proposed in this article. The effectiveness of the proposed solution is tested in simulation with mobile manipulator agents in cooperation with a group of human agents.