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Chin. Phys. B, 2015, Vol. 24(10): 100101    DOI: 10.1088/1674-1056/24/10/100101
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Rapid identifying high-influence nodes in complex networks

Song Boa, Jiang Guo-Pingb, Song Yu-Rongb, Xia Ling-Linga
a School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
b School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Abstract  A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method (RIM) to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered (SIR) model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness, and eigenvector centrality methods.
Keywords:  high-influence nodes      dynamic model      complex networks      centrality measures     
Received:  11 February 2015      Published:  05 October 2015
PACS:  01.75.+m (Science and society)  
  89.70.Eg (Computational complexity)  
  89.75.Fb (Structures and organization in complex systems)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61374180 and 61373136), the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project, China (Grant No. 12YJAZH120), and the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. RLD201212).
Corresponding Authors:  Jiang Guo-Ping     E-mail:

Cite this article: 

Song Bo, Jiang Guo-Ping, Song Yu-Rong, Xia Ling-Ling Rapid identifying high-influence nodes in complex networks 2015 Chin. Phys. B 24 100101

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