Importance evaluation method of complex network nodes based on information entropy and iteration factor

In the study of complex networks, researchers have long focused on identification influencing nodes. Based topological information, several quantitative methods determining importance nodes are proposed. K-shell is an efficient way to find potentially affected However, overemphasizes influence location central nodebut ignores effect force located at periphery network. Furthermore, topology real networks complex, which makes computation problem for large scale-free extremely difficult. order avoid ignoring contribution any node in network propagation, this work proposes improved method based iteration factor and information entropy estimate propagation capability each layer This not only achieves accuracy ordering, but also effectively avoids phenomenon rich clubs. To evaluate performance method, SIR model used simulate efficiency node, algorithm compared with other algorithms. Experimental results show that has better than suitable large-scale networks.