Adaptive rumor propagation and activity contagion in higher-order networks

Rumors in social systems are omnipresent. While traditional models focus on pairwise interactions, the collective effects of group interactions are insufficiently explored. Here we present a rumor propagation model on higher-order networks that incorporates 2-simplex structures and adaptive transitions between active and passive individuals. We find that higher-order networks substantially lower the propagation threshold and intensify nonlinear spreading effects. Active individuals are key drivers of rumor propagation and persistence. With active contagion, we observe that higher-order propagation increases peak and steady-state densities of active spreaders, thus extending the propagation and lifespan of rumors. We also apply a sequential quadratic programming algorithm to optimize the parameters of our model and validate its accuracy and applicability on real-world data. These results advance our understanding of contagion in higher-order social networks and support the design of targeted strategies for rumor mitigation.