Department of Mathematics, Southeast University, Nanjing 210096, China.
Chaos. 2011 Jun;21(2):025116. doi: 10.1063/1.3602220.
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.
在本文中，我们提出了一种用于复杂网络（特别是社交网络）的高效意见控制策略。所提出的自适应桥接控制（ABC）策略需要控制一种特殊类型的节点，称为桥接节点，并且不需要节点度或任何其他全局或局部知识，而某些其他免疫策略（包括有针对性的免疫和熟人免疫）则需要这些知识。我们研究了所提出的 ABC 策略在随机网络、小世界网络、无标度网络以及通过边交换方法调整的随机网络上的效率。我们的结果表明，所提出的 ABC 策略对于这四种类型的网络都是有效的。通过通过边交换方法调整聚类系数，发现我们的 ABC 策略的效率与聚类系数密切相关。本文的主要贡献可以列举如下：（1）提出了一种新的高阶社交网络来描述意见动态。（2）提出了一种算法，该算法不需要节点度和其他全局/局部网络结构信息的知识，从而可以更准确地控制“桥梁”，并进一步控制社交网络的意见动态。通过数值示例说明了我们的 ABC 策略的效率。（3）数值结果表明，我们的 ABC 策略对于聚类系数较高的网络更为有效。
