Self-stabilizing algorithms for Connected Vertex Cover and Clique decomposition problems

In many wireless networks, there is no fixed physical backbone nor centralized network management. The nodes of such a network have to self-organize in order to maintain a virtual backbone used to route messages. Moreover, any node of the network can be a priori at the origin of a malicious attack. Thus, in one hand the backbone must be fault-tolerant and in other hand it can be useful to monitor all network communications to identify an attack as soon as possible. We are interested in the minimum \emph{Connected Vertex Cover} problem, a generalization of the classical minimum Vertex Cover problem, which allows to obtain a connected backbone. Recently, Delbot et al.~\cite{DelbotLP13} proposed a new centralized algorithm with a constant approximation ratio of $2$ for this problem. In this paper, we propose a distributed and self-stabilizing version of their algorithm with the same approximation guarantee. To the best knowledge of the authors, it is the first distributed and fault-tolerant algorithm for this problem. The approach followed to solve the considered problem is based on the construction of a connected minimal clique partition. Therefore, we also design the first distributed self-stabilizing algorithm for this problem, which is of independent interest

Implementation and Comparison of Heuristics for the Vertex Cover Problem on Huge Graphs

International audienceWe present in this paper an experimental study of six heuristics for a well-studied NP-complete graph problem: the vertex cover. These algorithms are adapted to process huge graphs. Indeed, executed on a current laptop computer, they offer reasonable CPU running times (between twenty seconds and eight hours) on graphs for which sizes are between 200 *106 and 100 *109 vertices and edges. We have run algorithms on specific graph families (we propose generators) and also on random power law graphs. Some of these heuristics can produce good solutions. We give here a comparison and an analysis of results obtained on several instances, in terms of quality of solutions and complexity, including running times