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Finding k most influential edges on flow graphs

机译:在流程图上找到k个最有影响力的边缘

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摘要

In this paper, we formulate a novel question on maximum flow queries. Specifically, this problem aims to find which k edges would have the largest impact on a maximum flow query on a network. This problem has important applications in areas like social network and network planning. We show the inapproximability of the problems and present our heuristic algorithms. Experimental evaluations are carried out on real datasets and results show that our algorithms are scalable and return high quality solutions.
机译:在本文中,我们针对最大流量查询提出了一个新颖的问题。具体而言,此问题旨在查找哪些k边缘将对网络上的最大流量查询产生最大影响。该问题在社交网络和网络规划等领域具有重要的应用。我们展示了问题的逼近性,并提出了启发式算法。对真实数据集进行了实验评估,结果表明我们的算法具有可扩展性并返回了高质量的解决方案。

著录项

  • 来源
    《Information Systems》 |2017年第4期|93-105|共13页
  • 作者单位

    Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China;

    Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China;

    Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China;

    Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China;

    Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China;

    Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Graph;

    机译:图形;
  • 入库时间 2022-08-18 02:47:40

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