# Learning of networked spreading models from noisy and incomplete data

arXiv: https://arxiv.org/abs/2401.00011 · HTML: https://mlsanity.com/paper/2401.00011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Inferring Graphs from Cascades: A Sparse Recovery Framework](https://mlsanity.com/paper/1505.05663/) (2015)
- [Sketch-based Influence Maximization and Computation: Scaling up with Guarantees](https://mlsanity.com/paper/1408.6282/) (2014)
- [Trace Complexity of Network Inference](https://mlsanity.com/paper/1308.2954/) (2013)
- [Scalable Influence Estimation in Continuous-Time Diffusion Networks](https://mlsanity.com/paper/1311.3669/) (2013)
- [Reconstructing parameters of spreading models from partial observations](https://mlsanity.com/paper/1608.08698/) (2016)
- [Network reconstruction from infection cascades](https://mlsanity.com/paper/1609.00432/) (2016)
- [Inferring the origin of an epidemic with a dynamic message-passing algorithm](https://mlsanity.com/paper/1303.5315/) (2013)
- [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)
- [A Bayesian generative neural network framework for epidemic inference problems](https://mlsanity.com/paper/2111.03383/) (2021)
- [A message-passing approach for recurrent-state epidemic models on networks](https://mlsanity.com/paper/1505.02192/) (2015)
- [Optimal Deployment of Resources for Maximizing Impact in Spreading Processes](https://mlsanity.com/paper/1608.08278/) (2016)
- [Outward Influence and Cascade Size Estimation in Billion-scale Networks](https://mlsanity.com/paper/1704.04794/) (2017)
- [Learning Graphs from Noisy Epidemic Cascades](https://mlsanity.com/paper/1903.02650/) (2019)
- [Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs](https://mlsanity.com/paper/2303.17403/) (2023)
- [Scalable Influence Estimation Without Sampling](https://mlsanity.com/paper/1912.12749/) (2019)
- [Dynamic message-passing equations for models with unidirectional dynamics](https://mlsanity.com/paper/1407.1255/) (2014)
- [Learning Network Structures from Contagion](https://mlsanity.com/paper/1705.10051/) (2017)

## Built on by (most cited first, 2 of 2)

- [Learning hidden cascades via classification](https://mlsanity.com/paper/2505.11228/) (2025)
- [Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics](https://mlsanity.com/paper/2610.04033/) (2026)

## Abstract (arXiv)

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, we introduce a universal learning method based on scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.