Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 850-860.

### Node Importance Ranking of Complex Network based on Degree and Network Density

Hui Xua, b, Jianpei Zhanga, *, Jing Yanga, and Lijun Lunc

1. a College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China;
b Library, Heilongjiang University of Chinese Medicine, Harbin, 150040, China;
c College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China
• Submitted on  ;  Revised on  ;
• Contact: zhangjianpei@hrbeu.edu.cn
• About author:Hui Xu is studying for a doctorate in the College of Computer Science and Technology at Harbin Engineering University. She works in Heilongjiang University of Chinese Medicine. Her research interests are social computing, data mining technology, and software theory.Jianpei Zhang is a professor in the College of Computer Science and Technology at Harbin Engineering University. He is the director of the Institute of Computer Software and Theory of Harbin Engineering University. He has long been engaged in database theory and application, data mining technology, software theory, and other aspects of teaching and research work. Jing Yang is a professor in the College of Computer Science and Technology at Harbin Engineering University and an expert of the Harbin Informatization Committee. Her main research interests include database theory and application, data mining technology, knowledge database system, and software theory.Lijun Lun is a professor in the College of Computer Science and Information Engineering at Harbin Normal University. He teaches courses on operating systems and software engineering, object-oriented software engineering, advanced software engineering, and new software technologies. His research interests are software testing and software metrics.

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.