  In recent years, the exploration of node centrality has received significant
attention and extensive investigation, primarily fuelled by its applications in
diverse domains such as product recommendations, opinion propagation, disease
spread, and other scenarios requiring the maximization of node influence.
Despite various perspectives emphasizing the indispensability of higher-order
networks, research specifically delving into node centrality within the realm
of hypergraphs has been relatively constrained. This paper focuses on the
problem of influence maximization on the Simplicial Contagion Model (SCM),
using the susceptible-infected-recovered (SIR) model as an example. To find
practical solutions to this optimization problem, we have developed a
theoretical framework based on message passing process and conducted stability
analysis of equilibrium solutions for the self-consistent equations.
Furthermore, we introduce a metric called collective influence and propose an
adaptive algorithm, known as the Collective Influence Adaptive (CIA), to
identify influential propagators in the spreading process. Notably, our
algorithm distinguishes itself by prioritizing collective influence over
individual influence, resulting in demonstrably superior performance, a
characteristic substantiated by a comprehensive array of experiments.
