Hypernetwork disintegration with integrated metrics-driven evolutionary algorithm

Abstract

Network disintegration, which aims to degrade network functionality through the optimal set of node or edge removals, has been widely applied in various domains such as epidemic control and rumor containment. Hypernetworks are crucial and ubiquitous in capturing complex real-world higher-order interactions. However, existing network disintegration methods primarily focus on traditional pairwise networks, facing two significant challenges when dealing with hypernetworks: ineffective disruption of higher-order structures and limited capability in capturing higher-order features. To address these issues, we propose the Pre-Elite Multi-Objective Evolutionary Algorithm (PEEA), which identifies critical hyperedge set by optimizing two objectives: overall structure and higher-order disintegration. PEEA introduces weighted line graph to capture inter-hyperedge topological relationships and designs multi-scale importance metrics. It incorporates prior network information for elite individual initialization and optimizes target hyperedge set through multi-dimensional updates and selection operations. Simulation results show that PEEA improves the two objectives by 45.852% and 73.476%, demonstrating its effectiveness in hypernetwork disintegration. Further analysis of iterations ($T$) and crossover rate ($\beta$) indicates that PEEA achieves its most significant improvement in the first iteration, balancing fast convergence with accuracy.