一种以一维离散时间量子步行为灵感的方法,用于影响性节点识别
Wen Liang1, Yifan Wang1, Qiwei Liu2
1College of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
这项研究引入了一种新的量子启发方法,用于识别复杂网络中的有影响力的节点. 拟议的方法提高了准确性和可扩展性,为网络科学应用提供了一个实用的工具.
科学领域:
- 网络科学 网络科学
- 量子计算是一种量子计算.
- 复杂的系统复杂的系统.
背景情况:
- 识别有影响力的节点对于网络分析和弹性至关重要.
- 现有的量子步行方法在维度,效率和准确性方面面临挑战.
研究的目的:
- 提出一种新的量子启发方法,用于影响性节点的识别.
- 为了解决现有的基于量子步行方法的局限性.
主要方法:
- 引入了一种由一维离散时间量子步行 (IOQW) 启发的新方法.
- 开发了一个使用自动循环和网络连接的简化轮班操作员.
- 在硬币操作员中集成了高度的中心性和基于路径的功能.
主要成果:
- 与最先进的量子和经典方法相比,IOQW显示出更高的性能.
- 该方法有效地捕获了本地和全球的拓属性.
- 实现了O(Nk的低计算复杂度,提高了可扩展性.
结论:
- IOQW提供了一种强大而实用的工具,用于在复杂网络中识别有影响力的节点.
- 这项工作将量子启发的技术与实际的网络科学应用相结合.
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