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» A random graph model for massive graphs
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ECCV
2010
Springer
14 years 4 months ago
Graph Cut based Inference with Co-occurrence Statistics
Abstract. Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In t...
CP
2009
Springer
14 years 11 months ago
On the Structure of Industrial SAT Instances
Abstract. During this decade, it has been observed that many realworld graphs, like the web and some social and metabolic networks, have a scale-free structure. These graphs are ch...
Carlos Ansótegui, Jordi Levy, Maria Luisa B...
PVLDB
2010
127views more  PVLDB 2010»
13 years 5 months ago
Mining Significant Semantic Locations From GPS Data
With the increasing deployment and use of GPS-enabled devices, massive amounts of GPS data are becoming available. We propose a general framework for the mining of semantically me...
Xin Cao, Gao Cong, Christian S. Jensen
IJON
2007
81views more  IJON 2007»
13 years 11 months ago
Statistical analysis of spatially embedded networks: From grid to random node positions
Many conceptual studies of local cortical networks assume completely random wiring. For spatially extended networks, however, such random graph models are inadequate. The geometry...
Nicole Voges, Ad Aertsen, Stefan Rotter
AVI
2006
14 years 11 days ago
Just how dense are dense graphs in the real world?: a methodological note
This methodological note focuses on the edge density of real world examples of networks. The edge density is a parameter of interest typically when putting up user studies in an e...
Guy Melançon