# Learning hidden cascades via classification

arXiv: https://arxiv.org/abs/2505.11228 · HTML: https://mlsanity.com/paper/2505.11228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 14 of 14)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Dataset Condensation with Distribution Matching](https://mlsanity.com/paper/2110.04181/) (2021)
- [Graph Condensation via Receptive Field Distribution Matching](https://mlsanity.com/paper/2206.13697/) (2022)
- [Deep learning of contagion dynamics on complex networks](https://mlsanity.com/paper/2006.05410/) (2020)
- [Finding Patient Zero: Learning Contagion Source with Graph Neural Networks](https://mlsanity.com/paper/2006.11913/) (2020)
- [Inferring Graphs from Cascades: A Sparse Recovery Framework](https://mlsanity.com/paper/1505.05663/) (2015)
- [Reconstructing parameters of spreading models from partial observations](https://mlsanity.com/paper/1608.08698/) (2016)
- [Bayesian Inference of Spreading Processes on Networks](https://mlsanity.com/paper/1709.08862/) (2017)
- [Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations](https://mlsanity.com/paper/2007.06557/) (2020)
- [Optimal Deployment of Resources for Maximizing Impact in Spreading Processes](https://mlsanity.com/paper/1608.08278/) (2016)
- [Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization](https://mlsanity.com/paper/2010.01400/) (2020)
- [Dynamic Network Model from Partial Observations](https://mlsanity.com/paper/1805.10616/) (2018)
- [Message passing methods on complex networks](https://mlsanity.com/paper/2211.05054/) (2022)
- [Learning of networked spreading models from noisy and incomplete data](https://mlsanity.com/paper/2401.00011/) (2023)

## Built on by (most cited first, 1 of 1)

- [Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics](https://mlsanity.com/paper/2610.04033/) (2026)

## Abstract (arXiv)

The spreading dynamics in social networks are often studied under the assumption that individuals' statuses, whether informed or infected, are fully observable. However, in many real-world situations, such statuses remain unobservable, which is crucial for determining an individual's potential to further spread the infection. While final statuses are hidden, intermediate indicators such as symptoms of infection are observable and provide useful representations of the underlying diffusion process. We propose a partial observability-aware Machine Learning framework to learn the characteristics of the spreading model. We term the method Distribution Classification, which utilizes the power of classifiers to infer the underlying transmission dynamics. Through extensive benchmarking against Approximate Bayesian Computation and GNN-based baselines, our framework consistently outperforms these state-of-the-art methods, delivering accurate parameter estimates across diverse diffusion settings while scaling efficiently to large networks. We validate the method on synthetic networks and extend the study to a real-world insider trading network, demonstrating its effectiveness in analyzing spreading phenomena where direct observation of individual statuses is not possible.