Prediction of information diffusion probabilities for independent cascade model
International conference on knowledge-based and intelligent information and …, 2008•Springer
We address a problem of predicting diffusion probabilities in complex networks. As one
approach to this problem, we focus on the independent cascade (IC) model, and define the
likelihood for information diffusion episodes, where an episode means a sequence of newly
active nodes. Then, we present a method for predicting diffusion probabilities by using the
EM algorithm. Our experiments using a real network data set show the proposed method
works well.
approach to this problem, we focus on the independent cascade (IC) model, and define the
likelihood for information diffusion episodes, where an episode means a sequence of newly
active nodes. Then, we present a method for predicting diffusion probabilities by using the
EM algorithm. Our experiments using a real network data set show the proposed method
works well.
Abstract
We address a problem of predicting diffusion probabilities in complex networks. As one approach to this problem, we focus on the independent cascade (IC) model, and define the likelihood for information diffusion episodes, where an episode means a sequence of newly active nodes. Then, we present a method for predicting diffusion probabilities by using the EM algorithm. Our experiments using a real network data set show the proposed method works well.
Springer