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Identification of key players in networks using multi-objective optimization and its applications

机译：基于多目标优化的网络关键参与者识别方法及其应用

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摘要

In this dissertation, a new perspective for key player identification is proposed, based on optimizing multiple objectives of interest. The proposed approach is useful in identifying both key nodes and key edges in networks. Experimental results show that the sets of key players which optimize multiple objectives perform better than the key players identified using existing algorithms, in multiple applications such as eventual influence limitation problem, immunization problem, improving the fault tolerance of the smart grid, etc.;We utilize multi-objective optimization algorithms to optimize a set of objectives for a particular application. A large number of solutions are obtained when the number of objectives is high and the objectives are uncorrelated. But decision-makers usually require one or two solutions for their applications. In addition, the computational time required for multi-objective optimization increases with the number of objectives. A novel approach to obtain a subset of the Pareto optimal solutions is proposed and shown to alleviate the aforementioned problems.;As the size and the complexity of the networks increase, so does the computational effort needed to compute the network analysis measures. We show that degree centrality based network sampling can be used to reduce the running times without compromising the quality of key nodes obtained. (Abstract shortened by ProQuest.).
机译：本文在优化多个目标目标的基础上，提出了一种关键角色识别的新视角。所提出的方法可用于识别网络中的关键节点和关键边缘。实验结果表明，在最终影响限制问题，免疫问题，提高智能电网的容错性等多种应用中，优化多个目标的关键参与者的表现要优于使用现有算法确定的关键参与者。利用多目标优化算法为特定应用优化一组目标。当目标数量很多且目标不相关时，可以获得大量解决方案。但是决策者通常需要为其应用程序提供一种或两种解决方案。另外，多目标优化所需的计算时间随着目标数量的增加而增加。提出了一种新颖的获取Pareto最优解子集的方法，该方法可缓解上述问题。随着网络规模和复杂性的增加，计算网络分析度量所需的计算量也随之增加。我们表明，基于程度中心性的网络采样可用于减少运行时间，而不会影响获得的关键节点的质量。 （摘要由ProQuest缩短。）。

著录项

  • 作者

    Gunasekara, Raigamage Chulaka.;

  • 作者单位

    Syracuse University.;

  • 授予单位 Syracuse University.;
  • 学科 Computer science.;Artificial intelligence.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 159 p.
  • 总页数 159
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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