AbstractBecause of the long duration, multiplicity of technical disciplines, large number of project stakeholders, and high levels of complexity and uncertainty, project risk propagation control in large engineering projects (LEPs) is an enormous challenge for project managers. Although previous research has attained many risk propagation achievements regarding complex systems, complex coupled system modeling ignores the heterogeneity of the organizational structure of the actual LEPs, which affects the reliability of the calculated risk propagation results. To bridge this gap, this paper abstracts the LEP structure into a multilayer heterogeneous network comprising the stakeholder network and the project schedule network and proposes a method for characterizing the coupling relationship between two layers of the heterogeneous network. Then, the multiple uncertainties in risk propagation are greatly considered, and a risk propagation model is established based on the multilayer heterogeneous network and improved related schedule risk analysis model (CSRAM). Finally, the proposed model is applied to determine the delayed payment risk propagation in an actual LEP to verify the feasibility of the proposed model. The results indicate the following: (1) the delayed payment risk of a stakeholder evolves into a delay in the entire project; (2) several groups of comparative simulation experiments show that the proposed model, which considers multiple uncertainties and actual networks, includes more comprehensive and valuable risk information; (3) the multiple uncertainties of risk propagation are gradually superimposed with the increase in the number of construction activities affected by risk propagation; and (4) controlling for risk factors that have a high degree of influence and a large negative impact is an effective measure for blocking risk propagation across multilayer networks. This research lays an important foundation for risk propagation control in LEPs and contributes to the extension of the current theory of risk propagation in complex systems.