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» Kronecker Graphs: An Approach to Modeling Networks
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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
DSS
2011
13 years 4 days ago
Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks
Abstract: Much has been written about word of mouth and customer behavior. Telephone call detail records provide a novel way to understand the strength of the relationship between ...
Torsten Dierkes, Martin Bichler, Ramayya Krishnan
BMCBI
2010
147views more  BMCBI 2010»
13 years 8 months ago
Learning biological network using mutual information and conditional independence
Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a revers...
Dong-Chul Kim, Xiaoyu Wang, Chin-Rang Yang, Jean G...
CGF
2008
110views more  CGF 2008»
13 years 8 months ago
TimeRadarTrees: Visualizing Dynamic Compound Digraphs
The evolution of dependencies in information hierarchies can be modeled by sequences of compound digraphs with edge weights. In this paper we present a novel approach to visualize...
Michael Burch, Stephan Diehl
PODC
2004
ACM
14 years 2 months ago
Analyzing Kleinberg's (and other) small-world Models
We analyze the properties of Small-World networks, where links are much more likely to connect “neighbor nodes” than distant nodes. In particular, our analysis provides new re...
Charles U. Martel, Van Nguyen