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» Kronecker Graphs: An Approach to Modeling Networks
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ICML
2003
IEEE
16 years 18 days 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
14 years 3 months 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»
14 years 12 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»
14 years 12 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
15 years 5 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