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Improved centrality indicators to characterize the nodal spreading capability in complex networks

机译:改进了中心标准,以表征复杂网络中的节点扩散能力

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In this paper, we deeply investigate the identification of influential spreaders in complex networks based on various centrality indices. At first, we introduce several frequently used centrality indices to characterize the node influence. Then, based on the standard SIR model, we integrate various centrality indicators into the characterization of the nodal spreading capability, and then starting from the gravitational centrality formula, we systematically compare the ranking similarity and monotonicity under various centrality algorithms over 6 real-world networks and Barabasi-Albert model networks. The extensive simulations indicate that the mixed measure of gravitational centrality combining the k-shell value and degree will display the best performance as far as the ranking results are concerned, in which the focal node used the k-shell value as his mass while his neighboring nodes viewed the degree value as their masses. The current results are beneficial for us to develop the effective methods to discover and protect the significant nodes within many networked systems. (C) 2018 Elsevier Inc. All rights reserved.
机译:在本文中,我们深入研究了基于各种中心指标的复杂网络中有影响力扩展仪的识别。首先,我们介绍了几种经常使用的中心性指标来表征节点影响。然后,基于标准SIR模型,我们将各种中心指标集成到节点扩散能力的表征中,然后从引力中心公式开始,我们系统地比较各个中心算法下的排名相似性和单调性,在6个现实网络上的各个中心算法下和Barabasi-Albert模型网络。广泛的模拟表明,只要排名结果所涉及的排名结果,将显示最佳性能的混合措施将显示最佳性能,其中焦点在他的邻近时使用k-shell值作为他的质量节点将学位值视为群众。目前的结果有利于我们开发发现和保护许多联网系统中的有效节点的有效方法。 (c)2018年Elsevier Inc.保留所有权利。

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