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.

翻译：网络科学家常使用复杂动态过程描述网络传染，但拟合传染模型的工具通常假设简单动态。本文通过开发一种非参数方法填补这一空白，该方法利用节点状态序列重构网络与动态过程，采用打破简单成对传染与复杂邻域传染之间二分法的模型。我们进而证明：当网络密集或动态饱和时，通过复杂传染视角观察更易重构网络；而在其他情况下，简单传染则更具优势。