存储器值:一个复杂网络中的所有边缘的累积动态贡献值
Siyang Jiang1, Jun-An Lu1, Jin Zhou1,2
1School of Mathematics and Statistics, Wuhan University, Hubei 430072, China.
Physical review. E
|June 22, 2024
概括
这项研究量化了个别边缘如何对网络的Fiedler值做出贡献,揭示了少数边缘可以主导网络动态. 这些贡献可以是积极的或消极的,为网络结构和行为提供新的见解.
科学领域:
- 网络科学 网络科学
- 图形理论 图形理论
- 动态系统 动态系统
背景情况:
- 费德勒值是网络拓和网络动态的一个关键指标.
- 了解边缘对Fiedler值的贡献对于网络分析至关重要.
研究的目的:
- 在指向复杂网络中量化每个边缘对Fiedler值的动态贡献.
- 建立个别边缘贡献与总体Fiedler价值之间的关系.
主要方法:
- 开发了一种方法来测量每个边缘的动态贡献.
- 使用左和右菲德勒向量来确定贡献符号.
- 调查的定向和非定向网络案例.
主要成果:
- 边缘贡献可以是正的或负的,由费德勒向量决定.
- 所有边缘贡献的总和等于Fiedler值.
- 一小部分边缘显著影响网络动态,其中一个边缘有时贡献超过90%.
结论:
- 引入了一种用于边缘级Fiedler值量化的新方法.
- 证明了特定边缘对网络动态的重大影响.
- 通过边缘贡献分析,为Fiedler价值提供了新的视角.
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