通过层次化的k-shell和扩展的社区集成来识别有影响力的节点
Feifei Wang1, Zejun Sun2, Guan Wang1
1School of Information Engineering, Pingdingshan University, Pingdingshan, 467000, China.
Scientific reports
|February 23, 2026
概括
本研究介绍了HKEN,这是一种用于识别复杂网络中具有影响力的节点的新算法. 通过将层次化的k-shell分解与扩展的邻里数据相结合,HKEN提高了准确性和效率.
科学领域:
- 复杂的网络分析.
- 网络科学 网络科学
- 计算社会科学 计算社会科学
背景情况:
- 识别有影响力的节点对于理解复杂网络至关重要.
- 现有的方法在平衡精度和计算效率方面面临挑战.
- 需要整合全球和本地网络属性的算法.
研究的目的:
- 提出HKEN,一个算法集成分层k-shell分解和扩展邻里信息.
- 提高影响性节点识别的准确性和计算效率.
- 根据基准方法验证HKEN的业绩.
主要方法:
- 优化了基层k-shell分解与动态节点权重.
- 扩大了邻里范围,并为传输距离引入了局部聚类系数.
- 使用雅卡德相似系数进行影响聚合策略.
主要成果:
- 在10个现实网络上,HKEN表现出了卓越的性能.
- 与SIR (易受感染-感染者-康复者) 模型的结果取得了更高的一致性.
- 提高了排名最高的有影响力的节点的传播能力.
结论:
- 在识别有影响力的节点方面,HKEN提供了更高的准确性.
- 该算法有效地平衡了准确性和计算效率.
- 在复杂的网络分析中,HKEN提供了一个强大的方法.
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