Clan embeddings into trees, and low treewidth graphs

In low distortion metric embeddings, the goal is to embed a host "hard"metric space into a "simpler"target space while approximately preserving pairwise distances. A highly desirable target space is that of a tree metric. Unfortunately, such embedding will result in a huge distortion. A celebrated bypass to this problem is stochastic embedding with logarithmic expected distortion. Another bypass is Ramsey-type embedding, where the distortion guarantee applies only to a subset of the points. However, both these solutions fail to provide an embedding into a single tree with a worst-case distortion guarantee on all pairs. In this paper, we propose a novel third bypass called clan embedding. Here each point x is mapped to a subset of points f(x), called a clan, with a special chief point ?(x)e f(x). The clan embedding has multiplicative distortion t if for every pair (x,y) some copy y?e f(y) in the clan of y is close to the chief of x: miny?e f(y)d(y?,?(x))? t· d(x,y). Our first result is a clan embedding into a tree with multiplicative distortion O(logn/?) such that each point has 1+? copies (in expectation). In addition, we provide a "spanning"version of this theorem for graphs and use it to devise the first compact routing scheme with constant size routing tables. We then focus on minor-free graphs of diameter prameterized by D, which were known to be stochastically embeddable into bounded treewidth graphs with expected additive distortion ? D. We devise Ramsey-type embedding and clan embedding analogs of the stochastic embedding. We use these embeddings to construct the first (bicriteria quasi-polynomial time) approximation scheme for the metric ?-dominating set and metric ?-independent set problems in minor-free graphs.