Informative labeling schemes for graphs
Theoretical Computer Science
This paper introduces the notion of informative labeling schemes for arbitrary graphs. Let f (W ) be a function on subsets of vertices W . An f labeling scheme labels the vertices of a weighted graph G in such a way that f (W ) can be inferred (or at least approximated) efficiently for any vertex subset W of G by merely inspecting the labels of the vertices of W, without having to use any additional information sources. A number of results illustrating this notion are presented in the paper. We
... begin by developing f labeling schemes for three functions f over the class of n-vertex trees. The first function, SepLevel, gives the separation level of any two vertices in the tree, namely, the depth of their least common ancestor. The second, LCA, provides the least common ancestor of any two vertices. The third, Center, yields the center of any three given vertices v 1 , v 2 , v 3 in the tree, namely, the unique vertex z connected to them by three edge-disjoint paths. All of these three labeling schemes use O(log 2 n)-bit labels, which is shown to be asymptotically optimal. Our main results concern the function Steiner(W ), defined for weighted graphs. For any vertex subset W in the weighted graph G, Steiner(W ) represents the weight of the Steiner tree spanning the vertices of W in G. Considering the class of n-vertex trees with M-bit edge weights, it is shown that for this class there exists a Steiner labeling scheme using O((M + log n) log n) bit labels, which is asymptotically optimal. It is then shown that for the class of arbitrary n-vertex graphs with M-bit edge weights, there exists an approximate-Steiner labeling scheme, providing an estimate (up to a factor of O(log n)) for the Steiner weight Steiner(W ) of a given set of vertices W, using O((M + log n)log 2 n) bit labels.