# Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations

arXiv: https://arxiv.org/abs/2007.06557 · HTML: https://mlsanity.com/paper/2007.06557/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Learning of networked spreading models from noisy and incomplete data](https://mlsanity.com/paper/2401.00011/) (2023)
- [Learning the Topology and Behavior of Discrete Dynamical Systems](https://mlsanity.com/paper/2402.11686/) (2024)
- [Learning hidden cascades via classification](https://mlsanity.com/paper/2505.11228/) (2025)
- [Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks](https://mlsanity.com/paper/2405.06884/) (2024)
- [Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics](https://mlsanity.com/paper/2610.04033/) (2026)

## Abstract (arXiv)

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.