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Identification of multi-spreader users in social networks for viral marketing

机译:识别社交网络中病毒传播的多传播者用户

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摘要

Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the methods that is used for identifying the nodes is to rank them based on k-shell decomposition. Nevertheless, one of the disadvantages of this method is that it assigns the same rank to the nodes of a shell. Another disadvantage of this method is that only one indicator is fairly used to rank the nodes. k-Shell is an approach that is used for ranking separate spreaders, yet it does not have enough efficiency when a group of nodes with maximum spreading needs to be selected; therefore, this method, alone, does not have enough efficiency. Accordingly, in this study a hybrid method is presented to identify the super spreaders based on k-shell measure. Afterwards, a suitable method is presented to select a group of superior nodes in order to maximize the spread of influence. Experimental results on seven complex networks show that our proposed methods outperforms other well-known measures and represents comparatively more accurate performance in identifying the super spreader nodes.
机译:在社交网络中,识别高扩展功率节点是一个有趣的问题。当大量节点出现时,寻找超级扩展器节点成为一项艰巨的任务,并且其中现有链接的数量也变得巨大。用于识别节点的方法之一是基于k壳分解对它们进行排序。但是,此方法的缺点之一是它为外壳的节点分配了相同的等级。该方法的另一个缺点是,仅使用一个指标来对节点进行排名。 k-Shell是一种用于对单独的扩展器进行排名的方法,但是当需要选择一组最大扩展的节点时,它的效率不足。因此,仅此方法就没有足够的效率。因此,在这项研究中,提出了一种基于k-shell量度来识别超级吊具的混合方法。此后,提出了一种合适的方法来选择一组上级节点,以使影响范围最大化。在七个复杂网络上的实验结果表明,我们提出的方法优于其他众所周知的方法,并且在识别超级扩展器节点方面表现出相对更准确的性能。

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