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96
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NIPS
2008
14 years 11 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
81
Voted
ICDM
2007
IEEE
175views Data Mining» more  ICDM 2007»
15 years 4 months ago
gApprox: Mining Frequent Approximate Patterns from a Massive Network
Recently, there arise a large number of graphs with massive sizes and complex structures in many new applications, such as biological networks, social networks, and the Web, deman...
Chen Chen, Xifeng Yan, Feida Zhu, Jiawei Han
90
Voted
WWW
2010
ACM
15 years 4 months ago
Measurement and analysis of an online content voting network: a case study of Digg
In online content voting networks, aggregate user activities (e.g., submitting and rating content) make high-quality content thrive through the unprecedented scale, high dynamics ...
Yingwu Zhu
SDM
2007
SIAM
143views Data Mining» more  SDM 2007»
14 years 11 months ago
Patterns of Cascading Behavior in Large Blog Graphs
How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we ...
Jure Leskovec, Mary McGlohon, Christos Faloutsos, ...
GECCO
2010
Springer
254views Optimization» more  GECCO 2010»
15 years 2 months ago
Evolving viral marketing strategies
One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determi...
Forrest Stonedahl, William Rand, Uri Wilensky