It has been recently shown that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs, with distortion Olog (g+1)) [Sidiropoulos, FOCS 2010]. This embedding can be computed in polynomial time, provided that a drawing of the input graph into a genus-g surface is given. We show how to compute the above embedding without having such a drawing. This implies a general reduction for solving problems on graphs of small genus, even when the drawing into a small genus surface is unknown. To the best of our knowledge, this is the first result of this type.
Inspired by Bartal's stochastic embedding of general metrics into trees @cite_1 , Indyk and Sidiropoulos @cite_9 showed that every metric on a graph of genus @math can be stochastically embedded into a planar graph with distortion @math (see Section for a formal definition of stochastic embeddings). The above bound was later improved by Borradaile, Lee, and Sidiropoulos @cite_12 , who obtained an embedding with distortion @math . Subsequently, Sidiropoulos @cite_14 gave an embedding with distortion @math , matching the @math lower bound from @cite_12 . The embeddings from @cite_9 , and @cite_14 can be computed in polynomial time, provided that the drawing of the graph into a small genus surface is given. Computing the embedding from @cite_12 requires solving an NP-hard problem, even when the drawing is given.
@cite_9 @cite_14 @cite_1 @cite_12
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It has been shown by Indyk and Sidiropoulos [IS07] that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs with distortion 2^O(g). This bound was later improved to O(g^2) by Borradaile, Lee and Sidiropoulos [BLS09]. We give an embedding with distortion O(log g), which is asymptotically optimal. Apart from the improved distortion, another advantage of our embedding is that it can be computed in polynomial time. In contrast, the algorithm of [BLS09] requires solving an NP-hard problem. Our result implies in particular a reduction for a large class of geometric optimization problems from instances on genus-g graphs, to corresponding ones on planar graphs, with a O(log g) loss factor in the approximation guarantee. Indyk and Sidiropoulos (2007) proved that any orientable graph of genus @math can be probabilistically embedded into a graph of genus @math with constant distortion. Viewing a graph of genus @math as embedded on the surface of a sphere with @math handles attached, Indyk and Sidiropoulos' method gives an embedding into a distribution over planar graphs with distortion @math , by iteratively removing the handles. By removing all @math handles at once, we present a probabilistic embedding with distortion @math for both orientable and non-orientable graphs. Our result is obtained by showing that the nimum-cut graph of Erickson and Har Peled (2004) has low dilation, and then randomly cutting this graph out of the surface using the Peeling Lemma of Lee and Sidiropoulos (2009). A probabilistic C-embedding of a (guest) metric M into a collection of(host) metrics M'1, ..., M'k is a randomized mapping F of M intoone of the M'1, ..., M'k such that, for any two points p,q in theguest metric: The distance between F(p) and F(q) in any M'i is not smaller thanthe original distance between p and q. The expected distance between F(p) and F(q) in (random) M'i is notgreater than some constant C times the original distance, for C≥ 1. The constant C is called the distortion of the embedding. Low-distortion probabilistic embeddings enable reducing algorithmicproblems over "hard" guest metrics into "easy" host metrics.We show that every metric induced by a graph of bounded genus can beprobabilistically embedded into planar graphs, with constant distortion. The embedding can be computed efficiently, given a drawing of the graphon a genus-g surface. We consider the problem of embedding general metrics into trees. We give the first non-trivial approximation algorithm for minimizing the multiplicative distortion. Our algorithm produces an embedding with distortion (c log n)O(√log Δ), where c is the optimal distortion, and Δ is the spread of the metric (i.e. the ratio of the diameter over the minimum distance). We give an improved O(1)-approximation algorithm for the case where the input is the shortest path metric over an unweighted graph. Moreover, we show that by composing our approximation algorithm for embedding general metrics into trees, with the approximation algorithm of [BCIS05] for embedding trees into the line, we obtain an improved approximation algorithm for embedding general metrics into the line. We also provide almost tight bounds for the relation between embedding into trees and embedding into spanning subtrees. We show that for any unweighted graph G, the ratio of the distortion required to embed G into a spanning subtree, over the distortion of an optimal tree embedding of G, is at most O(log n). We complement this bound by exhibiting a family of graphs for which the ratio is Ω(log n log log n).