Prediction-Centric Learning of Independent Cascade Dynamics from Partial
Observations
- URL: http://arxiv.org/abs/2007.06557v3
- Date: Sat, 24 Jul 2021 23:04:00 GMT
- Title: Prediction-Centric Learning of Independent Cascade Dynamics from Partial
Observations
- Authors: Mateusz Wilinski, Andrey Y. Lokhov
- Abstract summary: We address the problem of learning of a spreading model such that the predictions generated from this model are accurate.
We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach.
We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model.
- Score: 13.680949377743392
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. We focus on a challenging
setting where full observations of the dynamics are not available, and 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. The popular Independent Cascade model
is used to illustrate our approach. 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 the prediction quality.
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