Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (5): 140-146.doi: 10.11896/jsjkx.200300184

• Database & Big Data & Data Science • Previous Articles     Next Articles

Importance Evaluation Algorithm Based on Node Intimate Degree

MA Yuan-yuan, HAN Hua, QU Qian-qian   

  1. School of Science,Wuhan University of Technology,Wuhan 430070,China
  • Received:2020-03-31 Revised:2020-06-14 Online:2021-05-15 Published:2021-05-09
  • About author:MA Yuan-yuan,born in 1996,postgra-duate.Her main research interests include complex network and so on.(962031436@qq.com)
    HAN Hua,born in 1975,Ph.D,professor,master supervisor.Her main research interests include complex analysis,economic control and decision-ma-king.
  • Supported by:
    National Natural Science Foundation of China(11601402),Youth Program of National Natural Science Foundation of China(111701435) and Fundamental Research Funds for the Central Universities(2018IB016).

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Abstract: Identifying important nodes in complex networks has been a hot topic in the field of social network analysis and mi-ning,which helps to understand the role of influential communicators in information diffusion and the spread of infectious diseases.The existing algorithm of node importance takes neighbor information into account,but ignoring the structure information between node and neighbor node.To solve this problem,considering the different influence of the neighbor node to node under different structures,this paper proposes a node-importance evaluation algorithm that takes into account the number of neighbors of a node and the intimacy between nodes and neighbors,which embodies the degree of node and “intimate” attribute.In this algorithm,similarity index is used to measure the intimacy between nodes,and Kendall correlation coefficient is used to evaluate the accuracy of node ranking.The SIR(susceptible-infected-recovered) model is used to simulate the propagation process on several classical networks.The results show that compared with degree index,closeness centrality index,betweenness centrality index and K-shell index,KI index can rank the propagation influence of nodes more accurately.

Key words: Complex networks, Intimate degree, Node importance, Similarity

CLC Number: 

  • TP393
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