The adoption of individual behavioural patterns is largely determined by stimuli arriving from peers via social interactions or from external sources. Based on these influences, individuals are commonly assumed to follow simple or complex adoption rules, inducing social contagion processes. In reality, multiple adoption rules may coexist even within the same social contagion process, introducing additional complexity into the spreading phenomena. Our goal is to understand whether coexisting adoption mechanisms can be distinguished from a microscopic view, at the egocentric network level, without requiring global information about the underlying network, or the unfolding spreading process. We formulate this question as a classification problem, and study it through a Bayesian likelihood approach and with random forest classifiers in various synthetic and data-driven experiments. This study offers a novel perspective on the observations of propagation processes at the egocentric level and a better understanding of landmark contagion mechanisms from a local view.

翻译：个体行为模式的采纳很大程度上取决于来自同伴通过社会互动或外部来源的刺激。基于这些影响，个体通常被假定遵循简单或复杂的采纳规则，从而引发社会传染过程。实际上，即使在同一社会传染过程中也可能存在多种采纳规则，这为传播现象带来了额外的复杂性。我们的目标是理解能否从微观视角——即以自我为中心的网络层面——区分共存的采纳机制，而无需底层网络的全局信息或传播过程的完整演化。我们将此问题表述为分类问题，并通过贝叶斯似然方法和随机森林分类器在各种合成与数据驱动的实验中进行研究。本研究为自我中心层面的传播过程观测提供了新视角，并从局部视角深化了对关键传染机制的理解。