在引用网络中的超线性增长和节点衰老下,模拟动态三元关闭的演变
Li Liang1, Hao Liu1, Shi-Cai Gong1
1School of Sciences, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了引用网络的新模型,考虑了增长和老龄化. 该模型准确地复制了现实世界的网络结构,为知识传播动态提供了洞察力.
科学领域:
- 图书统计学 图书统计学
- 网络科学 网络科学
- 信息科学 信息科学 信息科学
背景情况:
- 引用网络对于理解知识流程至关重要.
- 现有的模型往往忽略了复杂的关系,如共同引用.
- 关键的经验特征包括超线性节点流入和影响的时间衰变.
研究的目的:
- 提出一个新的引文网络演化的模型.
- 将超线性增长和衰老纳入网络分析.
- 在科学合作中分析生长和衰老之间的相互作用.
主要方法:
- 超线性生长与衰老的三角进化模型 (TEM-SGA) 的发展.
- 将TEM-SGA模型与实证引用网络数据相匹配.
- 网络属性的结构分析,如度分布和聚类系数.
- 引入一种退化模型 (TEM-SG),不包括理论分析的衰老.
主要成果:
- 该TEM-SGA模型成功地复制了真实引用网络的关键结构性质.
- 衰老对网络结构的影响随规模而变化,并与增长相互作用.
- 加快增长可以减轻与衰老相关的网络中断.
- 在理论上,TEM-SG模型预测了几度的功率定律分布.
结论:
- 这项研究为引用网络的演变提供了一个强大的模型,考虑了三元关闭,扩张和衰老.
- 了解增长动态如何影响知识传播中的老龄化影响.
- 加强了分析复杂引用网络结构的理论基础.
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