Research on Cascade Propagation of Collaborative Innovation Risks in Industrial Clusters Considering Entities Heterogeneity - Shi, Xiaowei; Wang, Jifa; Wang, Yang (International journal of computational intelligence systems, 2025)
The objective of this study is to effectively mitigate the cascading propagation of collaborative innovation risks within industrial clusters and bolster the stability of their innovation networks. Drawing upon the cascade failure theory in complex networks, we employ the BA scale-free network model to construct an industrial cluster innovation network. We develop a cascade propagation model for collaborative innovation risk, addressing three dimensions: risk load, risk capacity, and load redistribution following node failures. To enhance the model, we propose a risk load distribution strategy that considers the heterogeneity among innovation entities, focusing on the similarity degree, importance degree, and cooperation degree of neighboring innovative entities. Through simulation experiments, we demonstrate that the integrated allocation strategy significantly improves the resistance to destruction of industrial cluster innovation networks. However, under intentional attacks, the resilience of these networks remains relatively weak. Further investigation reveals that the risk load capacity enhanced by the integrated allocation strategy can somewhat fortify the resistance of industrial cluster innovation networks to such attacks. The findings offer valuable insights for risk management and stability enhancement in industrial cluster innovation networks.