Distinguishing mechanisms of social contagion from local network view

TL;DR

This paper investigates whether different social contagion mechanisms can be distinguished from local network data using machine learning, without needing global network information, enhancing understanding of spreading processes.

Contribution

It introduces a classification approach to identify coexisting adoption mechanisms from egocentric network data, advancing analysis of social contagion complexity.

Findings

Successful classification of contagion mechanisms using Bayesian and random forest models

Effective distinction of multiple adoption rules from local network observations

Provides a new perspective on analyzing propagation processes at the individual level

Abstract

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.