Li Ping, Sun Xian, Zhang Kai, Zhang Jie, Small Michael
Center for Networked System, School of Computer Science, Southwest Petroleum University, Chengdu 610500, P. R. China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Aug;88(2):022817. doi: 10.1103/PhysRevE.88.022817. Epub 2013 Aug 28.
Suppressing harmful synchronization in complex networked systems is receiving increasing interest in various contexts including epileptic seizure and the Internet and traffic congestion. It is traditionally believed that by disrupting the most important nodes (i.e., those nodes of high degrees) synchronization can be effectively mitigated. However, this approach can perform poorly in more general situations such as homogeneous (random) or small world networks. In this article, we investigate how topological properties, such as the heterogeneity of the network can have an impact on its desynchronization. In particular, we propose a topology-aware scheme that chooses topologically sufficiently diversified contrarian nodes by taking into account the patterns of connectivity among them. Hence, a maximal number of nodes in the network can be influenced, leading to a more global impact and greater disruption to synchronization. Our scheme demonstrates significantly improved performance on various networked systems including homogeneous networks, small world, and even scale-free networks.
在包括癫痫发作、互联网和交通拥堵等各种背景下，抑制复杂网络系统中的有害同步受到越来越多的关注。传统观点认为，通过干扰最重要的节点（即那些度数高的节点），可以有效减轻同步现象。然而，在诸如均匀（随机）或小世界网络等更一般的情况下，这种方法可能效果不佳。在本文中，我们研究网络的拓扑特性，如网络的异质性如何对其去同步产生影响。特别是，我们提出了一种拓扑感知方案，该方案通过考虑节点之间的连接模式来选择拓扑上足够多样化的逆势节点。因此，网络中可以影响的节点数量达到最大，从而产生更全局的影响并对同步造成更大的干扰。我们的方案在包括均匀网络、小世界网络甚至无标度网络在内的各种网络系统上都表现出显著提高的性能。
