...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Influential node identification in a constrained greedy way
【24h】

Influential node identification in a constrained greedy way

机译：受限制贪婪方式的影响节点识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Influence maximization aims to identify a set of influential nodes as seeds to initiate the propagation of influence so that the influence can be spread most widely. It plays critical roles in the areas such as rumor controlling, viral marketing and so on. Greedy algorithms have been exploited to identify seeds. They have been proved to provide the results approximating the optimum. However, non-neglectable iterative calculations and influence overlap between seeds degrade the performance and the efficiency of those algorithms. To alleviate the performance and efficiency degradation, we propose an influence maximization approach based on the property of submodularity. The approach selects seeds according to the influence propagated in a constrained greedy way to alleviate the performance degradation caused by non-neglectable iterative calculations. It performs rigorous overlap cost checks on the nodes which could be taken as seeds to relieve the efficiency degradation caused by the influence overlap. The experiments verify the performance and the efficiency of the approach. (C) 2020 Elsevier B.V. All rights reserved.
机译：影响最大化旨在识别一组有影响力的节点作为种子，以引发影响的传播，使得影响可以最广泛传播。它在谣言控制，病毒营销等领域发挥着关键角色。贪婪的算法已被利用以识别种子。他们已被证明提供了近似最佳的结果。然而，种子之间的不可忽略的迭代计算和影响重叠降低了这些算法的性能和效率。为了缓解性能和效率退化，我们提出了一种基于潜水线性性质的最大化方法。该方法根据在受限制的贪婪方式中传播的影响选择种子，以减轻因不可忽视的迭代计算引起的性能降级。它在节点上执行严格的重叠成本检查，该节点可以被视为种子以减轻由影响重叠引起的效率降级。实验验证了这种方法的性能和效率。 （c）2020 Elsevier B.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号