Node importance ranking of complex network based on degree and network density

Abstract

Node importance ranking of complex networks is of great significance to the study of network robustness. The classical centrality measure degree can reflect the number of neighbors of a node, but it ignores the information between its neighbors. In order to mine the important nodes in the network accurately and efficiently, a method of ranking the node importance of complex networks based on multi-attribute evaluation and node deletion is proposed in this paper. Based on the degree attributes of the target node and its neighbors, this method introduces two attributes, which are the local network density centered on the target node and the assortativity coefficient. It takes into account the characteristics of the scale, tightness, and topology of the local area network where the node and its neighbors are located. This paper conducts deliberate attack experiments on four real networks. Through a comparison between the experimental results of the maximal connected coefficient and network efficiency, our approach is proven to be valid and feasible.