Results 1  10
of
8,764
Colour Critical Hypergraphs With Many Edges
, 2004
"... We show that for all k ≥ 3, r> l ≥ 2 there exists constant c = c(k, r, l) such that for large enough n there exists a kcolourcritical runiform hypergraph on less than n vertices, having more than cn l edges, and having no lset of vertices occurring in more than one edge. ..."
Abstract
 Add to MetaCart
We show that for all k ≥ 3, r> l ≥ 2 there exists constant c = c(k, r, l) such that for large enough n there exists a kcolourcritical runiform hypergraph on less than n vertices, having more than cn l edges, and having no lset of vertices occurring in more than one edge.
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
Abstract

Cited by 821 (1 self)
 Add to MetaCart
The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices
Construction of locally plane graphs with many edges
"... A graph drawn in the plane with straightline edges is called a geometric graph. If no path of length at most k in a geometric graph G is selfintersecting we call G klocally plane. The main result of this paper is a construction of klocally plane graphs with a superlinear number of edges. For th ..."
Abstract
 Add to MetaCart
A graph drawn in the plane with straightline edges is called a geometric graph. If no path of length at most k in a geometric graph G is selfintersecting we call G klocally plane. The main result of this paper is a construction of klocally plane graphs with a superlinear number of edges
LOCALIZATION IN RANDOM GEOMETRIC GRAPHS WITH TOO MANY EDGES
"... Abstract. Consider a random geometric graph G(χn, rn), given by connecting two vertices of a Poisson point process χn of intensity n on the unit torus whenever their distance is smaller than the parameter rn. The model is conditioned on the rare event that the number of edges observed, E, is great ..."
Abstract
 Add to MetaCart
Abstract. Consider a random geometric graph G(χn, rn), given by connecting two vertices of a Poisson point process χn of intensity n on the unit torus whenever their distance is smaller than the parameter rn. The model is conditioned on the rare event that the number of edges observed, E
How bad is selfish routing?
 JOURNAL OF THE ACM
, 2002
"... We consider the problem of routing traffic to optimize the performance of a congested network. We are given a network, a rate of traffic between each pair of nodes, and a latency function for each edge specifying the time needed to traverse the edge given its congestion; the objective is to route t ..."
Abstract

Cited by 657 (27 self)
 Add to MetaCart
We consider the problem of routing traffic to optimize the performance of a congested network. We are given a network, a rate of traffic between each pair of nodes, and a latency function for each edge specifying the time needed to traverse the edge given its congestion; the objective is to route
Pregel: A system for largescale graph processing
 IN SIGMOD
, 2010
"... Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational model ..."
Abstract

Cited by 496 (0 self)
 Add to MetaCart
Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational
Surface Simplification Using Quadric Error Metrics
"... Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplifi ..."
Abstract

Cited by 1174 (16 self)
 Add to MetaCart
Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
Abstract

Cited by 1277 (120 self)
 Add to MetaCart
edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two
Comparing Images Using the Hausdorff Distance
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1993
"... The Hausdorff distance measures the extent to which each point of a `model' set lies near some point of an `image' set and vice versa. Thus this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. In this paper we provide ef ..."
Abstract

Cited by 659 (10 self)
 Add to MetaCart
(translation and rotation). The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors as occur with edge detectors and other feature extraction methods. Moreover
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
, 2005
"... How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include hea ..."
Abstract

Cited by 541 (48 self)
 Add to MetaCart
How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include
Results 1  10
of
8,764