Open AccessA sorting method of node based on Eigenvector and Closeness centrality
Author(s) -
Jingcheng Zhu,
Lunwen Wang,
Tao Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012043
Subject(s) - centrality , betweenness centrality , closeness , node (physics) , sorting , computer science , katz centrality , data mining , network science , network theory , theoretical computer science , complex network , mathematics , algorithm , statistics , engineering , mathematical analysis , structural engineering , world wide web
Accurately identifying influential nodes in a complex network is of great significance to information dissemination. At present, researchers have put forward methods such as Degree centrality, Betweenness centrality, Closeness centrality and Eigenvector centrality, but these methods have certain limitations. There are many factors that affect the results of the node sorting, such as the number of neighbor nodes, node location information, etc. If multiple factors are combined, the proposed method can show more characteristics. First, closeness centrality of neighboring nodes is accumulated to reflect the influence of location information, and then integrated with Eigenvector centrality, the ECCN centrality is proposed to identify the network Influential nodes, this method integrates the path length and the number and quality of neighbor nodes. The SIR propagation model is used to simulate the influence of nodes on multiple real networks, and all centralities are compared with the results of the SIR model. Experimental analysis shows that the proposed method can more accurately identify influential nodes than other methods.