# Matrix Product Belief Propagation for reweighted stochastic dynamics over graphs

arXiv: https://arxiv.org/abs/2303.17403 · HTML: https://mlsanity.com/paper/2303.17403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Learning of networked spreading models from noisy and incomplete data](https://mlsanity.com/paper/2401.00011/) (2023)
- [A short review on the maximum clique problem algorithms with classical, AI, and quantum methods](https://mlsanity.com/paper/2403.09742/) (2024)
- [Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics](https://mlsanity.com/paper/2610.04033/) (2026)

## Abstract (arXiv)

Stochastic processes on graphs can describe a great variety of phenomena ranging from neural activity to epidemic spreading. While many existing methods can accurately describe typical realizations of such processes, computing properties of extremely rare events is a hard task. Particularly so in the case of recurrent models, in which variables may return to a previously visited state. Here, we build on the matrix product cavity method, extending it fundamentally in two directions: first, we show how it can be applied to Markov processes biased by arbitrary reweighting factors that concentrate most of the probability mass on rare events. Second, we introduce an efficient scheme to reduce the computational cost of a single node update from exponential to polynomial in the node degree. Two applications are considered: inference of infection probabilities from sparse observations within the SIRS epidemic model, and the computation of both typical observables and large deviations of several kinetic Ising models.