Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing , and opinion setting. Recent real-world spreading events further highlight the need for prediction, optimization, and control of diffusion dynamics. To tackle these tasks, it is essential to learn the effective spreading model and transmission probabilities across the network of interactions. However, in most cases the transmission rates are unknown and need to be inferred from the spreading data. Additionally, full observation of the dynamics is rarely available. As a result, standard approaches such as maximum likelihood quickly become intractable for large network instances. In this work, we study the popular Independent Cascade model of stochastic diffusion dynamics. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach, which is able to learn parameters of the effective spreading model given only limited information on the activation times of nodes in the network. Importantly, we show that the resulting model approximates the marginal activation probabilities that can be used for prediction of the spread .
Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting. We address the problem of learning of a spreading model such that the predictions generated from this model are accurate and could be subsequently used for the optimization, and control of diffusion dynamics. Unfortunately, full observations of the dynamics are rarely available. As a result, standard approaches such as maximum likelihood quickly become intractable for large network instances. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach, which is able to learn parameters of the effective spreading model given only limited information on the activation times of nodes in the network. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model. We develop a systematic procedure for learning a mixture of models which further improves prediction quality of the model .
D
,
R
Recent real-world spreading events further highlight the need for prediction,
We address the problem of learning of a spreading model such that the predictions generated from this model are accurate and could be subsequently used for the
R
To tackle these tasks, it is essential to learn the effective spreading model and transmission probabilities across the network of interactions. However, in most cases the transmission rates are unknown and need to be inferred from the spreading data. Additionally, full observation
Unfortunately, full observations
R
is
are
D
In this work, we study the popular Independent Cascade model of stochastic diffusion dynamics.
R
Importantly, we show that the resulting model approximates the marginal activation probabilities that can be used for prediction of the spread
We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model. We develop a systematic procedure for learning a mixture of models which further improves prediction quality of the model