In this paper, we present an original network graph embedding to speed-up distance-range and k-nearest neighbor queries in (weighted) graphs. Our approach implements the paradigm ...
Abstract— In this paper we analyze the positioning of landmarks in coordinates-based Internet distance prediction approaches with focus on Global Network Positioning (GNP). We sh...
A method is presented to partition a given set of data entries embedded in Euclidean space by recursively bisecting clusters into smaller ones. The initial set is subdivided into ...
Nonlinear dimensionality reduction is formulated here as the problem of trying to find a Euclidean feature-space embedding of a set of observations that preserves as closely as p...
We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-d...