# Reconstructing networks from simple and complex contagions

arXiv: https://arxiv.org/abs/2405.00129 · HTML: https://mlsanity.com/paper/2405.00129/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Configuring Random Graph Models with Fixed Degree Sequences](https://mlsanity.com/paper/1608.00607/) (2016)
- [Deep learning of contagion dynamics on complex networks](https://mlsanity.com/paper/2006.05410/) (2020)
- [Network reconstruction and community detection from dynamics](https://mlsanity.com/paper/1903.10833/) (2019)
- [Bayesian inference of network structure from information cascades](https://mlsanity.com/paper/1908.03318/) (2019)
- [Modelling the transmission dynamics of online social contagion](https://mlsanity.com/paper/1602.00248/) (2016)

## Abstract (arXiv)

Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics. Here, we address this gap by developing a nonparametric method to reconstruct a network and dynamics from a series of node states, using a model that breaks the dichotomy between simple pairwise and complex neighborhood-based contagions. We then show that a network is more easily reconstructed when observed through the lens of complex contagions if it is dense or the dynamic saturates, and that simple contagions are better otherwise.