Rapid identifying high-influence nodes in complex networks

doi:10.1088/1674-1056/24/10/100101

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
Revised: 27 April 2015
Accepted manuscript online:

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: jianggp@njupt.edu.cn

Cite this article:

Song Bo (宋波), Jiang Guo-Ping (蒋国平), Song Yu-Rong (宋玉蓉), Xia Ling-Ling (夏玲玲) Rapid identifying high-influence nodes in complex networks 2015 Chin. Phys. B 24 100101

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