Efficient measurement model for critical nodes based on edge clustering coefficients and edge betweenness

Identifying critical nodes is vital for optimizing network structure and enhancing network robustness in complex networks. The concepts of edge betweenness and edge clustering coefficients are based on node betweenness and the node clustering coefficient. This paper proposes a new measurement model for critical nodes based on global features and local features, which considers the edge betweenness and edge clustering coefficients and combines the mutual influence between nodes and edges in a network. Subsequently, an algorithm based on the aforementioned model is proposed. The proposed algorithm is evaluated on the ARPA network, and it is proven to be effective in determining the importance of nodes. Another experiment is performed on a scale-free network, in which the accuracy of the algorithm is compared with other algorithms. Experimental results prove that the proposed algorithm is robust under deliberate attacks.