时间网络的时间依赖的个性化PageRank:离散和连续的尺度.
David Aleja1,2,3,4, Julio Flores1, Eva Primo1,2
1Departamento de Matemática Aplicada, Ciencia e Ingeniería de los Materiales y Tecnología Electrónica, Universidad Rey Juan Carlos, 28933 Móstoles (Madrid), Spain.
Chaos (Woodbury, N.Y.)
|August 30, 2024
概括
本研究为不断发展的网络引入了时间变化的PageRank,显示离散抽样准确估计连续时间网络的PageRank. 它精确地限制了个性化向量对节点排名的影响.
科学领域:
- 网络科学 网络科学
- 计算机科学 计算机科学
- 数据分析 数据分析
背景情况:
- 随着时间的推移而变化的时间网络,为PageRank等排名算法带来了独特的挑战.
- 现有的PageRank方法通常假定静态的网络结构,限制了它们对动态系统的适用性.
研究的目的:
- 为时间网络引入和分析一个依赖时间的PageRank算法.
- 调查时间变化的网络拓,缓解因子和个性化向量对节点排名的影响.
- 提供个性化载体在时间网络中的影响的理论界限.
主要方法:
- 开发了连续时间时间网络的PageRank公式.
- 通过抽样分析了连续时间和离散时间时间网络PageRank之间的关系.
- 建立了依赖时间的个性化向量对节点重要性影响的理论界限.
主要成果:
- 证明了连续时间网络的PageRank可以使用离散时间样本准确估计.
- 提供了个性化向量的个人节点排名影响的精确边界.
- 在PageRank中展示了网络拓学的独立时间变量,缓解因子和个性化向量.
结论:
- 拟议的依赖时间的PageRank为分析动态网络提供了一个强大的方法.
- 离散采样为连续时间网络分析提供了可靠的近似值.
- 这项工作促进了对不断变化的网络结构中排名的理解.
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