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» Rigorous Result for the CHKNS Random Graph Model
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ICML
2003
IEEE
14 years 9 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
CVPR
2010
IEEE
14 years 2 months ago
Semantic Context Modeling with Maximal Margin Conditional Random Fields for Automatic Image Annotation
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources,...
Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-seng chu...
IM
2008
13 years 8 months ago
Directed Random Dot Product Graphs
In this paper we consider three models for random graphs that utilize the inner product as their fundamental object. We analyze the behavior of these models with respect to cluster...
Stephen J. Young, Edward R. Scheinerman
APPROX
2010
Springer
179views Algorithms» more  APPROX 2010»
13 years 10 months ago
Rumor Spreading on Random Regular Graphs and Expanders
Broadcasting algorithms are important building blocks of distributed systems. In this work we investigate the typical performance of the classical and well-studied push model. Assu...
Nikolaos Fountoulakis, Konstantinos Panagiotou
CORR
2006
Springer
95views Education» more  CORR 2006»
13 years 8 months ago
Naming Games in Spatially-Embedded Random Networks
We investigate a prototypical agent-based model, the Naming Game, on random geometric networks. The Naming Game is a minimal model, employing local communications that captures th...
Qiming Lu, Gyorgy Korniss, Boleslaw K. Szymanski