Dariusz Dereniowski

Drawing maps with advice

We study the problem of the amount of information required to draw a complete or a partial map of a graph with unlabeled nodes and arbitrarily labeled ports. A mobile agent, starting at any node of an unknown connected graph and walking in it, has to accomplish one of the following tasks: draw a complete map of the graph, i.e., find an isomorphic copy of it including port numbering, or draw a partial map, i.e., a spanning tree, again with port numbering. The agent executes a deterministic algorithm and cannot mark visited nodes in any way. None of these map drawing tasks is feasible without any additional information, unless the graph is a tree. Hence we investigate the minimum number of bits of information (minimum size of advice) that has to be given to the agent to complete these tasks. It turns out that this minimum size of advice depends on the number n of nodes or the number m of edges of the graph, and on a crucial parameter @m, called the multiplicity of the graph, which measures the number of nodes that have an identical view of the graph. We give bounds on the minimum size of advice for both above tasks. For @m=1 our bounds are asymptotically tight for both tasks and show that the minimum size of advice is very small. For @m>1 the minimum size of advice increases abruptly. In this case our bounds are asymptotically tight for topology recognition and asymptotically almost tight for spanning tree construction.

From Pathwidth to Connected Pathwidth

It is proven that the connected pathwidth of any graph

Edge ranking and searching in partial orders

G

Edge ranking of weighted trees

is at most

Vertex rankings of chordal graphs and weighted trees

2\cdot\textup{pw}(G)+1

Bounds on the cover time of parallel rotor walks

, where

Leader Election for Anonymous Asynchronous Agents in Arbitrary Networks

\textup{pw}(G)

Fast collaborative graph exploration

is the pathwidth of

Cholesky Factorization of Matrices in Parallel and Ranking of Graphs

G

Collaborative Exploration by Energy-Constrained Mobile Robots

. The method is constructive, i.e., it yields an efficient algorithm that for a given path decomposition of width