Distinguishing mechanisms of social contagion from local network view.

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 to 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 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.