Importance ranking method of multiple attributes of network nodes

At present, most of the ranking algorithms for the importance degree of nodes in complex networks consider the local or global attributes of nodes unilaterally, which affects the accuracy of the ranking of the importance degree of nodes. Therefore, in this paper, based on the node degree, node position in the social network and other multiattribute comprehensive considerations, we proposed a node importance ranking algorithm for node degree, KS value and KS center iteration times(KSCIT), using the entropy value method to determine the weights of the node degree, KS value and KSCIT and using real social network experiments. Experimental results show that this algorithm is more accurate than other sorting algorithms that consider the unilateral attributes of nodes.