Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network

Correctly measuring the importance of nodes in a complex network is critical for studying robustness network, and designing security policy based on these highly important can effectively improve aspects such as data Internet or hardening traffic hubs. Currently included are degree centrality, closeness clustering coefficient, H-index. Although indicators identify to some extent, they influenced by single evaluation perspective have limitations, so most existing methods cannot fully reflect node information. In this paper, we propose multi-attribute critic decision indicator (MCNDI) CRITIC method, considering H-index, k-shell indicator, constraint coefficient. This method integrates information attributes from multiple perspectives provides more comprehensive measure importance. An experimental analysis Chesapeake Bay contiguous USA shows that MCNDI has better ranking monotonicity, stable metric results, adaptable topology. Additionally, deliberate attack simulations real networks showed exhibits high convergence speed attacks USAir97 technology routes networks.