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Found 5 papers, 0 papers with code
Horde of Bandits using Gaussian Markov Random Fields
The gang of bandits (GOB) model \cite{cesa2013gang} is a recent contextual bandits framework that shares information between a set of bandit problems, related by a known (possibly noisy) graph.
Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?
A disadvantage of this setting is that the marketer is forced to select all the seeds based solely on a diffusion model.
Social and Information Networks
From Competition to Complementarity: Comparative Influence Diffusion and Maximization
We study two natural optimization problems, Self Influence Maximization and Complementary Influence Maximization, in a novel setting with complementary entities.
Social and Information Networks Physics and Society H.2.8
Influence Maximization with Bandits
We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to.
A Data-Based Approach to Social Influence Maximization
In particular, we introduce a new model, which we call credit distribution, that directly leverages available propagation traces to learn how influence flows in the network and uses this to estimate expected influence spread.
Databases
