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» Kronecker Graphs: An Approach to Modeling Networks
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SIGECOM
2006
ACM
143views ECommerce» more  SIGECOM 2006»
14 years 2 months ago
Braess's paradox in large random graphs
Braess’s Paradox is the counterintuitive but well-known fact that removing edges from a network with “selfish routing” can decrease the latency incurred by traffic in an eq...
Gregory Valiant, Tim Roughgarden
BMCBI
2010
178views more  BMCBI 2010»
13 years 8 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
IJCNN
2007
IEEE
14 years 3 months ago
Risk Assessment Algorithms Based on Recursive Neural Networks
— The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In thi...
Alejandro Chinea Manrique De Lara, Michel Parent
RECOMB
2010
Springer
14 years 3 months ago
Incremental Signaling Pathway Modeling by Data Integration
Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology. Pathway model construction is often an inherently incremental...
Geoffrey Koh, David Hsu, P. S. Thiagarajan
CORR
2012
Springer
230views Education» more  CORR 2012»
12 years 4 months ago
Fast Triangle Counting through Wedge Sampling
Graphs and networks are used to model interactions in a variety of contexts, and there is a growing need to be able to quickly assess the qualities of a graph in order to understa...
C. Seshadhri, Ali Pinar, Tamara G. Kolda