A hybrid centrality method revealing structural robustness of complex networks

Ensuring structural robustness is a fundamental goal in complex network analysis and control. Existing approaches primarily focus on either local connectivity or information diffusion, often neglecting the critical roles of nodes in preserving overall network integrity. To address this limitation, we propose a hybrid centrality method (HCM) integrating local and global network information to quantify node importance. Specifically, we define local dispersion centrality by combining node degree and local clustering coefficient to capture the dispersion of a node's neighborhood, and employ betweenness centrality to reflect its global structural significance as bridge nodes. HCM is formulated as a weighted combination of these two measures, with a tunable parameter balancing local and global contributions. It comprehensively assesses node importance and effectively identifies nodes whose removal fragments the network. Extensive experiments on synthetic and real-world networks demonstrate HCM outperforms baselines in network dismantling, with pronounced effectiveness in high-clustering networks.