Key Node Recognition Algorithm based on Improved K-shell entropy

Accurate identification of key nodes in complex networks plays an important role in the stability of network structure and information transmission. The traditional K-shell method only evaluates the importance of nodes by their location in the network, which results in low discrimination. Therefore, considering the influence of global and local information on the importance of nodes, a key node recognition algorithm based on improved K-shell entropy is proposed. The method defines the entropy of nodes through the Ks value of the neighborhood of nodes, thus reflecting the K-shell distribution characteristics of the neighborhood nodes. Simulation experiments on several network datasets show that the proposed method can identify and distinguish key nodes in complex networks more accurately.