Social contagion models on hypergraphs

TL;DR

This paper develops an analytical framework for social contagion dynamics on hypergraphs, revealing complex behaviors like phase transitions, bi-stability, and hysteresis, thus advancing higher-order network modeling.

Contribution

It introduces a novel analytical approach to study social contagion on hypergraphs, extending beyond pairwise interactions and exploring rich dynamical phenomena.

Findings

Model exhibits first and second-order phase transitions.

Identification of bi-stability and hysteresis in social contagion.

Extension of latent heat concept to social dynamics.

Abstract

Our understanding of the dynamics of complex networked systems has increased significantly in the last two decades. However, most of our knowledge is built upon assuming pairwise relations among the system's components. This is often an oversimplification, for instance, in social interactions that occur frequently within groups. To overcome this limitation, here we study the dynamics of social contagion on hypergraphs. We develop an analytical framework and provide numerical results for arbitrary hypergraphs, which we also support with Monte Carlo simulations. Our analyses show that the model has a vast parameter space, with first and second-order transitions, bi-stability, and hysteresis. Phenomenologically, we also extend the concept of latent heat to social contexts, which might help understanding oscillatory social behaviors. Our work unfolds the research line of higher-order models and the analytical treatment of hypergraphs, posing new questions and paving the way for modeling dynamical processes on these networks.