Abstract.A graph G is δ-hyperbolic if for any four vertices u, v, x, y of G the two larger of the three distance sums dG(u, v) + dG(x, y), dG(u, x) + dG(v, y), dG(u, y) + dG(v, x) differ by at most δ, and the smallest δ 0 for which G is δ-hyperbolic is called the hyperbolicity of G. In this paper, we construct a distance labeling scheme for bounded hyperbolicity graphs, that is a vertex labeling such that the distance between any two vertices of G can be estimated from their labels, without any other source of information. More precisely, our scheme assigns labels of O(log 2 n) bits for bounded hyperbolicity graphs with n vertices such that distances can be approximated within an additive error of O(log n). The label length is optimal for every additive error up to n ε . We also show a lower bound of Ω(log log n) on the approximation factor, namely every s-multiplicative approximate distance labeling scheme on bounded hyperbolicity graphs with polylogarithmic labels requires s = Ω(log log n).
International audienceThis paper presents the BINCOA framework, whose goal is to ease the development of binary code analysers by providing an open formal model for low-level programs (typically: executable files), an XML format for easy exchange of models and some basic tool support. The BINCOA framework already comes with three different analysers, including simulation, test generation and Control-Flow Graph reconstruction
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