一个节点影响排名算法,结合k-shell代和节点度的结合
Yating Ji1, Lequn Liu1, Shujia Li1,2
1Information Management Office, Hefei Normal University, Hefei, China.
PloS one
|July 24, 2025
概括
本研究引入了一种新的算法,用于识别复杂网络中的关键节点. 它通过结合K-shell代,节点程度和邻居信息来改进现有方法,以获得更准确的影响排名.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 数据挖掘 数据挖掘
背景情况:
- 识别关键节点对于理解复杂网络中的信息/疾病传播至关重要.
- 传统的K-shell分解缺乏歧视,因为仅依赖全球位置.
- 现有的方法无法同时使用代因子和节点区分的度数.
研究的目的:
- 提出一个新的节点影响排名算法.
- 通过整合全球和本地网络拓信息来增强K分解.
- 在复杂网络中提高关键节点识别的准确性和区别性.
主要方法:
- 开发了一个新的算法,集成K-shell代,节点程度和邻居信息.
- 在八个不同的网络上通过模拟实验评估了算法.
- 与已建立的方法 (如dc,bc,cc,k-shell,KS+,KSIF,LGI和DCK) 进行性能比较.
主要成果:
- 与现有方法相比,拟议的算法在排名关键节点方面表现出卓越的准确性.
- 与第二个最佳算法相比,实现了平均精度提高5.15%.
- 该KTD算法在识别顶级关键节点和强大的区分能力方面显示出更高的准确性.
结论:
- 综合方法为关键节点识别提供了更高的准确性和区分能力.
- 该算法保持良好的时间性能,使其适合大型复杂网络.
- 这种方法为网络分析应用提供了更强大的解决方案.
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