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A fast algorithm for finding most influential people based on the linear threshold model

机译：基于线性阈值模型的快速找到最有影响力的人的算法

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摘要

Finding the most influential people is an NP-hard problem that has attracted many researchers in the field of social networks. The problem is also known as influence maximization and aims to find a number of people that are able to maximize the spread of influence through a target social network. In this paper, a new algorithm based on the linear threshold model of influence maximization is proposed. The main benefit of the algorithm is that it reduces the number of investigated nodes without loss of quality to decrease its execution time. Our experimental results based on two well-known datasets show that the proposed algorithm is much faster and at the same time more efficient than the state of the art algorithms.
机译：寻找最有影响力的人是一个NP难题，已经吸引了社交网络领域的许多研究人员。这个问题也被称为影响力最大化，旨在找到一些能够通过目标社交网络最大化影响力传播的人。提出了一种基于影响最大线性阈值模型的新算法。该算法的主要好处是，它减少了被调查节点的数量，而没有质量损失，从而减少了其执行时间。我们基于两个著名的数据集的实验结果表明，与现有技术相比，该算法速度更快，效率更高。

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  • 来源
    《Expert Systems with Application》 |2015年第3期|1353-1361|共9页
  • 作者

    Khadije Rahimkhani; Abolfazl Aleahmad; Maseud Rahgozar; Ali Moeini;

  • 作者单位

    Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran;

    Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran;

    Database Research Group, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran;

    Faculty of Engineering Science, School of Engineering, University of Tehran, Iran;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Social networks; Influential people retrieval; Influence maximization; Linear threshold model;

    机译：社交网络;有影响力的人员检索;影响力最大化;线性阈值模型;

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