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» Reconstructing Randomized Social Networks
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CVPR
2009
IEEE
1382views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Super-Resolution via Recapture and Bayesian Effect Modeling
This paper presents Bayesian edge inference (BEI), a single-frame super-resolution method explicitly grounded in Bayesian inference that addresses issues common to existing meth...
Bryan S. Morse, Dan Ventura, Kevin D. Seppi, Neil ...
ICASSP
2009
IEEE
14 years 2 months ago
Field inversion by consensus and compressed sensing
— We study the inversion of a random field from pointwise measurements collected by a sensor network. We assume that the field has a sparse representation in a known basis. To ...
Aurora Schmidt, José M. F. Moura
KDD
2007
ACM
168views Data Mining» more  KDD 2007»
14 years 8 months ago
Finding tribes: identifying close-knit individuals from employment patterns
We present a family of algorithms to uncover tribes--groups of individuals who share unusual sequences of affiliations. While much work inferring community structure describes lar...
Lisa Friedland, David Jensen
ICDM
2010
IEEE
230views Data Mining» more  ICDM 2010»
13 years 5 months ago
Clustering Large Attributed Graphs: An Efficient Incremental Approach
In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectivenes...
Yang Zhou, Hong Cheng, Jeffrey Xu Yu
CCR
2010
133views more  CCR 2010»
13 years 7 months ago
The role of network trace anonymization under attack
In recent years, academic literature has analyzed many attacks on network trace anonymization techniques. These attacks usually correlate external information with anonymized data...
Martin Burkhart, Dominik Schatzmann, Brian Trammel...