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» A random graph model for massive graphs
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COLING
2010
15 years 1 months ago
FactRank: Random Walks on a Web of Facts
Fact collections are mostly built using semi-supervised relation extraction techniques and wisdom of the crowds methods, rendering them inherently noisy. In this paper, we propose...
Alpa Jain, Patrick Pantel
IBPRIA
2007
Springer
16 years 7 days ago
Bayesian Oil Spill Segmentation of SAR Images Via Graph Cuts
Abstract. This paper extends and generalizes the Bayesian semisupervised segmentation algorithm [1] for oil spill detection using SAR images. In the base algorithm on which we buil...
Sónia Pelizzari, José M. Bioucas-Dia...
INFOCOM
2006
IEEE
16 years 3 days ago
Sampling Techniques for Large, Dynamic Graphs
— Peer-to-peer systems are becoming increasingly popular, with millions of simultaneous users and a wide range of applications. Understanding existing systems and devising new pe...
Daniel Stutzbach, Reza Rejaie, Nick G. Duffield, S...
SIAMDM
2008
79views more  SIAMDM 2008»
15 years 6 months ago
Testing Triangle-Freeness in General Graphs
In this paper we consider the problem of testing whether a graph is triangle-free, and more generally, whether it is H-free, for a fixed subgraph H. The algorithm should accept gr...
Noga Alon, Tali Kaufman, Michael Krivelevich, Dana...
SAC
2002
ACM
15 years 5 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...