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» Generating Random Graphs with Large Girth
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AI
2006
Springer
14 years 9 months ago
Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering
This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. ...
Ole J. Mengshoel, David C. Wilkins, Dan Roth
LATIN
2004
Springer
15 years 3 months ago
Embracing the Giant Component
Consider a game in which edges of a graph are provided a pair at a time, and the player selects one edge from each pair, attempting to construct a graph with a component as large ...
Abraham Flaxman, David Gamarnik, Gregory B. Sorkin
104
Voted
SAC
2002
ACM
14 years 9 months ago
A mobility and traffic generation framework for modeling and simulating ad hoc communication networks
We present a generic mobility and traffic generation framework that can be incorporated into a tool for modeling and simulating large scale ad hoc networks. Three components of thi...
Christopher L. Barrett, Madhav V. Marathe, James P...
FGR
2008
IEEE
346views Biometrics» more  FGR 2008»
15 years 4 months ago
Markov random field models for hair and face segmentation
This paper presents an algorithm for measuring hair and face appearance in 2D images. Our approach starts by using learned mixture models of color and location information to sugg...
Kuang-chih Lee, Dragomir Anguelov, Baris Sumengen,...
87
Voted
TOG
2002
133views more  TOG 2002»
14 years 9 months ago
Interactive motion generation from examples
There are many applications that demand large quantities of natural looking motion. It is difficult to synthesize motion that looks natural, particularly when it is people who mus...
Okan Arikan, David A. Forsyth