对图形数据的基于结构的节点重要性排名方法
1School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650504, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究引入了一种新的结构法,用于在图形数据中对节点进行排名. 新方法有效地利用了本地和全球图形结构信息,优于现有方法.
科学领域:
- 图形理论是指图形的理论.
- 网络分析 网络分析
- 数据科学是数据科学.
背景情况:
- 在图形数据中排名节点对于许多应用程序至关重要.
- 经典方法经常忽视全球图形结构,只关注本地信息.
- 需要使用包含本地和全球结构性质的排名方法.
研究的目的:
- 为图形数据开发一个高效的节点重要性排名方法.
- 调查纳入当地和全球结构信息的影响.
- 为节点排名提出基于结构的方法.
主要方法:
- 提出了一种基于的新型结构方法.
- 该方法涉及去除目标节点及其边缘.
- 结构是通过考虑本地和全球图形结构信息来计算的.
主要成果:
- 提出的基于结构的方法与五种基准方法进行了评估.
- 该方法在八个现实世界数据集中表现出强的性能.
- 实验结果证实了整合本地和全球结构信息的有效性.
结论:
- 基于结构的节点重要性排名方法是有效和高效的.
- 结合本地和全球结构信息可以提高节点排名的准确性.
- 这种方法为分析复杂图形数据提供了有价值的工具.
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