Reconstructing networks from simple and complex contagions

TL;DR

This paper introduces a nonparametric method for reconstructing networks and their contagion dynamics from observed node states, bridging the gap between simple and complex contagion models.

Contribution

It develops a unified approach that does not assume simple or complex dynamics, improving network reconstruction across different contagion types.

Findings

Dense networks and saturated dynamics favor complex contagion reconstruction.

Simple contagions are easier to reconstruct in sparse networks or with less saturation.

The method outperforms traditional models in diverse network conditions.

Abstract

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.