Maximizing the Spread of Influence via Generalized Degree Discount.
Maximizing the Spread of Influence via Generalized Degree Discount.
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DOI：
10.1371/journal.pone.0164393
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Yi D
中科院分区：
文献类型：
--
作者：
Wang X;Zhang X;Zhao C;Yi D
如何识别出一小部分有影响力的传播者来控制网络中的传播过程是一个至关重要的基础性问题。在以前的研究中，基于度的启发式称为DegreeDiscount已被证明可以有效地识别多个有影响力的传播者，并已作为基准方法。然而，DegreeDiscount的基本假设是不够的，因为它平等地对待所有节点，没有任何区别。为了考虑真实的世界网络的一般情况，本文提出了一种新的启发式方法广义度折扣，作为原方法的有效扩展。在该方法中，节点的状态被定义为不受其邻居影响的概率，并提出了一个节点的广义折扣度指标来衡量它所能影响的节点的期望数量。然后根据其在当前网络中的广义折扣度依次选择传播器。在四个真实的网络上进行了实验，结果表明，该方法识别出的传播者比几种基准方法更有影响力。最后，我们分析了我们的方法和三个常见的度为基础的方法之间的关系。
It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods.
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影响因子：
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作者：
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DOI：
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