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的动态过程来描述网络传染现象，但拟合传染模型的工具通常假设简单的动态机制。本文通过开发一种非参数方法来解决这一差距，该方法利用一个打破简单成对传染与基于邻域的复杂传染二分法的模型，从一系列节点状态中重建网络及其动态过程。我们进而证明，当网络密集或动态过程趋于饱和时，通过复杂传染的视角更容易重建网络；反之，简单传染则更具优势。