Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations

TL;DR

This paper introduces a scalable, prediction-focused learning algorithm for the Independent Cascade model that effectively learns from partial observations, improving prediction accuracy and enabling better control of diffusion processes.

Contribution

A novel dynamic message-passing algorithm for learning spreading models from limited data, enhancing prediction accuracy and scalability in large networks.

Findings

Improved prediction of marginal probabilities over the original model.

Efficient learning from partial observations using scalable algorithms.

Enhanced prediction accuracy with a mixture of models.

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.