通过本地信息进行更高阶链接预测
Bo Liu1,2, Rongmei Yang1, Linyuan Lü1,2,3
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.
Chaos (Woodbury, N.Y.)
|August 3, 2023
概括
本研究引入了两种新方法,即简化分解权重和封闭比重,用于预测复杂网络中未来的高阶相互作用. 这些基于局部特征的方法在高阶链接预测方面优于现有的基准标准.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 数据挖掘 数据挖掘
背景情况:
- 高级链接预测对于准确建模复杂系统至关重要.
- 传统的对联网络缺乏高级网络的细节.
- 由于网络的复杂性,预测更高层次的链接具有挑战性.
研究的目的:
- 利用本地特征开发高效简洁的高阶链接预测算法.
- 引入新的相似度指标,用于预测未来的高阶相互作用 (simples) 在simplicial网络.
主要方法:
- 提出了两个相似度指标:简单分解重量和闭合比重量.
- 这些指标通过简单分解和集群状态捕获本地更高阶信息.
- 评估了八个实证简化网络的性能.
主要成果:
- 拟议的指标在预测第三级和第四级相互作用方面优于现有基准.
- 这些算法在各种训练集大小中展示了强大的性能.
- 当地特征对于更高层次的链接预测是有利的.
结论:
- 新的指标为更高阶链接预测提供了一种有效的方法.
- 这些发现凸显了局部特征在复杂网络分析中的重要性.
- 拟议的算法为未来网络科学研究提供了一个有希望的方向.
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