The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance me...

Many tractable algorithms for solving the Constraint Satisfaction Problem (Csp) have been developed using the notion of the treewidth of some graph derived from the input Csp instance. In particular, the incidence graph of the Csp instance is one such graph. We introduce the notion of an incidence graph for modal logic formulas in a certain normal form. We investigate the parameterized complexi...

In this paper, we address the problem of consistency checking for Euclidean spatial constraints. A dimension graph representation is pmposed to maintain the Euclidean spatial constraints among objects. The basic idea is to project the spatial constraints on both X and Y dimensions, and to construct a dimension graph on each dimension. Using a dimension graph representation transfonns the proble...

The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance me...

Given a planar graph derived from a spherical, euclidean or hyperbolic tessellation, one can define a discrete curvature by combinatorial properties, which after embedding the graph in a compact 2d-manifold, becomes the Gaussian curvature .

Symmetric disk graphs are often used to model wireless communication networks. Given a set S of n points in R (representing n transceivers) and a transmission range assignment r : S → R, the symmetric disk graph of S (denoted SDG(S)) is the undirected graph over S whose set of edges is E = {(u, v) | r(u) ≥ |uv| and r(v) ≥ |uv|}, where |uv| denotes the Euclidean distance between points u and v. ...

Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral ...

Several application fields require finding Euclidean coordinates consistent with a set of distances. More precisely, given a simple undirected edge-weighted graph, we wish to find a realization in a Euclidean space so that adjacent vertices are placed at a distance which is equal to the corresponding edge weight. Realizations of a graph can be either flexible or rigid. In certain cases, rigidit...

Bunn, Urban and Keitt[12] discuss landscape connectivity in the Coastal Plain of North Carolina through a graph theoretic approach using focal-species analysis. Graph Theory is very much useful in the study of landscape connectivity using graph as an ecological construct. Different nodes (vertices) represent habitat patches and edges, the distance (functional distance and not Euclidean distance...

Specific emitter identification (SEI) is a technology for extracting fingerprint features from signal and identifying the emitter. In this paper, author proposes an SEI method based on ensemble neural networks (ENN) graphs, with following innovations: First, graph used to show data in non-Euclidean space. Namely, sequence constructed into transform Euclidian space Hence, feature (the of space) ...