通过局部代指标预测节点影响.
Shilun Zhang1, Alan Hanjalic1, Huijuan Wang2
1Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands.
Scientific reports
|February 28, 2024
概括
通过使用代指标来改进预测网络中的节点影响,这些指标逐渐纳入全球信息. 低级度量通常是足够的,达到与复杂方法相比的结果.
科学领域:
- 网络科学 网络科学
- 复杂的系统复杂的系统.
- 数据分析 数据分析
背景情况:
- 节点传播影响是网络激活的关键.
- 结合本地和全球网络信息,可以提高影响力预测.
- 以前的方法通常依赖于单一的中心性指标.
研究的目的:
- 调查本地与全球拓信息对节点影响预测的贡献.
- 确定相对局部网络信息是否足以准确预测影响.
- 提出和评估一个代的度量集来预测节点影响.
主要方法:
- 杆代过程类似于自向量中心性计算.
- 定义了一个代的度量集 (顺序1到K) 编码渐进的全球信息.
- 评估了三种代指标,这些指标与经典的中心性指标相融合.
- 在现实世界,合成和空间嵌入式网络上进行测试.
主要成果:
- 预测质量与低阶代指标 (K < 5) 迅速趋同.
- 随着K的进一步增加,预测质量只会得到微不足道的改善.
- 最好的代指标集 (K < 5) 与使用七个中心性指标的基准方法竞争.
- 具有大直径的空间嵌入网络需要更高阶的代度量来实现可比性能.
结论:
- 代度量集提供高效和有效的预测节点传播的影响.
- 通过低级代指标捕获的相对局部网络信息通常是足够的.
- 拟议的代方法为复杂的多度量方法提供了有竞争力的替代方案.
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