# Network reconstruction and community detection from dynamics

## TL;DR

This paper introduces a scalable Bayesian approach that simultaneously reconstructs network structures and detects communities from observed dynamics, improving accuracy through their mutual reinforcement.

## Contribution

It presents a novel nonparametric Bayesian method that jointly infers network topology and community structure from functional data, enhancing both tasks.

## Key findings

- Effective on synthetic and empirical networks
- Improves reconstruction accuracy via community detection
- Applicable to epidemic and Ising model data

## Abstract

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.