优化搜索流程与随机重置在虚分数尺度免费的网络上的虚分数尺度
Yongjin Chen1, Zhenhua Yuan1, Long Gao1
1School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China; Guangdong Provincial Key Laboratory of Information Security Technology, Guangzhou University, Guangzhou 510006, China; and Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou 510006, China.
Physical review. E
|January 20, 2024
概括
在无尺度网络上优化搜索涉及随机步行与重置. 重置到更高程度或更接近目标的节点可以提高搜索效率,特别是在重置到随机静止分布节点时.
科学领域:
- 网络科学 网络科学
- 复杂的系统复杂的系统.
- 统计物理 统计物理
背景情况:
- 伪分形无尺度网络 (PSFW) 是具有小世界属性的无尺度网络的关键模型.
- 了解网络动态对于各种现实世界的过程至关重要.
研究的目的:
- 研究重置位置对PSFW搜索效率的影响.
- 优化搜索过程使用离散时间随机步行与重置.
主要方法:
- 在PSFW模型上利用离散时间随机步行.
- 分析了目标节点的平均首次通道时间 (MFPT).
- 探索了各种重置位置和概率 (γ).
主要成果:
- 搜索效率受到重置位置的程度和距离目标的距离的显著影响.
- 更高度的重置节点和更短的距离改善了融合.
- 从静态分布中重置到一个节点大大加快了目标获取.
结论:
- 战略重置显著提高了在没有大规模网络中的搜索效率.
- 结果为优化在未知目标场景中的随机搜索提供了洞察力.
- 这项研究有助于理解复杂网络模型中的动态.
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