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UAI
2003
14 years 8 days ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
CISS
2008
IEEE
13 years 11 months ago
Detection of anomalous meetings in a social network
Abstract-- When monitoring interactions within a social network, meetings or contacts between different members of the network are recorded. This paper addresses the problem of usi...
Jorge Silva, Rebecca Willett
CORR
2008
Springer
97views Education» more  CORR 2008»
13 years 11 months ago
Efficient Algorithms and Routing Protocols for Handling Transient Single Node Failures
Single node failures represent more than 85% of all node failures in the today's large communication networks such as the Internet [10]. Also, these node failures are usually...
Amit M. Bhosle, Teofilo F. Gonzalez
DATAMINE
2010
175views more  DATAMINE 2010»
13 years 11 months ago
Extracting influential nodes on a social network for information diffusion
We address the combinatorial optimization problem of finding the most influential nodes on a large-scale social network for two widely-used fundamental stochastic diffusion models...
Masahiro Kimura, Kazumi Saito, Ryohei Nakano, Hiro...
EOR
2010
99views more  EOR 2010»
13 years 11 months ago
Min sum clustering with penalties
Traditionally, clustering problems are investigated under the assumption that all objects must be clustered. A shortcoming of this formulation is that a few distant objects, calle...
Refael Hassin, Einat Or