# Network reconstruction and community detection from dynamics

arXiv: https://arxiv.org/abs/1903.10833 · HTML: https://mlsanity.com/paper/1903.10833/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 13 of 13)

- [Unifying Pairwise Interactions in Complex Dynamics](https://mlsanity.com/paper/2201.11941/) (2022)
- [Network reconstruction via the minimum description length principle](https://mlsanity.com/paper/2405.01015/) (2024)
- [Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths](https://mlsanity.com/paper/2112.00183/) (2021)
- [Scalable network reconstruction in subquadratic time](https://mlsanity.com/paper/2401.01404/) (2024)
- [Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables](https://mlsanity.com/paper/2301.04088/) (2023)
- [Module-based regularization improves Gaussian graphical models when observing noisy data](https://mlsanity.com/paper/2303.16796/) (2023)
- [Deep Graph Learning will stall without Network Science](https://mlsanity.com/paper/2502.01177/) (2025)
- [Uncertainty quantification and posterior sampling for network reconstruction](https://mlsanity.com/paper/2503.07736/) (2025)
- [Node metadata can produce predictability transitions in network inference problems](https://mlsanity.com/paper/2103.14424/) (2021)
- [Geodesic Length Distribution in Sparse Network Ensembles](https://mlsanity.com/paper/2111.02330/) (2021)
- [Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior](https://mlsanity.com/paper/2210.15410/) (2022)
- [Machine learning of network inference enhancement from noisy measurements](https://mlsanity.com/paper/2309.02050/) (2023)
- [Reconstructing networks from simple and complex contagions](https://mlsanity.com/paper/2405.00129/) (2024)

## Abstract (arXiv)

We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.