基于优化超相邻矩阵的时间网络的节点重要性识别
Rui Liu1, Sheng Zhang1, Donghui Zhang1
1School of Information Engineering, Nanchang Hangkong University, 696 Fenghe South Avenue, Nanchang 330063, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一个优化的超相邻矩阵 (OSAM) 来分析时间网络. OSAM方法增强了节点重要性识别,在现实世界网络数据集中显示了更好的消息传播和覆盖范围.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
背景情况:
- 确定节点的重要性对于理解时间网络动态至关重要.
- 像SAM和SSAM这样的现有方法在捕捉复杂的时间网络结构方面存在局限性.
研究的目的:
- 为时间网络提出一个优化的超相邻矩阵 (OSAM) 建模方法.
- 为了准确地表达时间网络结构和节点的重要性,考虑到内部和层间的关系.
主要方法:
- 开发了一个优化的超相邻矩阵 (OSAM),用于层内关系的边缘权重.
- 使用定向图形特征建模定向层间关系.
- 计算节点重要性,使用基于跨层自向量中心性的索引.
主要成果:
- 在OSAM方法准确地表示时间网络结构.
- 与SAM和SSAM相比,OSAM展示了更快的消息传播和更大的消息覆盖范围.
- 实现了更好的SIR (易受感染-感染-恢复) 和NDCG@10 (正常化折扣累积收益) 指标.
结论:
- OSAM方法提供了一个更有效的方法,用于在时间网络中识别节点重要性.
- 由于OSAM能够考虑加权和定向关系,因此在动态网络分析中提高了其性能.
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