  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.
