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No Access Submitted: 31 January 2011 Accepted: 01 June 2011 Published Online: 28 June 2011

Adaptive bridge control strategy for opinion evolution on social networks

Chaos 21, 025116 (2011); https://doi.org/10.1063/1.3602220
Cheng Qian1,2, a), Jinde Cao1, b), Jianquan Lu1,3, c), and Jürgen Kurths3,4,5, d)
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ABSTRACT
In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes’ degree and other global/local network structure information, is proposed to control the “bridges” more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.

ACKNOWLEDGMENTS

The work of J. Cao was supported by the National Natural Science Foundation of China under Grant Nos. 11072059 and 60874088, the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20070286003, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK2009271. The work of J. Q. Lu was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 11026182, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK2010408, the Innovation Fund of Basic Scientific Research Operating Expenses of Southeast University under Grant No. 3207010501, Program for New Century Excellent Talents in University (NCET-10-0329), and the Alexander von Humboldt Foundation of Germany. The work of J. Kurths was supported by SUMO (EU) and ECONS (WGL).

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