从有偏见的随机走路重建的网络中识别感知到的本地属性
Lucas Guerreiro1, Filipi Nascimento Silva2, Diego Raphael Amancio1
1Institute of Mathematics and Computer Science - USP, Avenida Trabalhador São-carlense, São Carlos, SP, Brazil.
PloS one
|January 19, 2024
概括
从符号序列中恢复隐藏的网络结构是可能的. 代理对高度节点的偏差改善了重建,除了集群系数和异常度,其中自我避开步行显示出竞争性表现.
科学领域:
- 网络科学 网络科学
- 数据分析数据分析
- 计算建模计算建模
背景情况:
- 现实世界的系统从穿越网络的代理产生的符号时间序列.
- 通常,底层网络结构是未知的,需要从观察到的序列中恢复.
- 应用包括文本分析,其中单词序列可能会揭示隐藏的语言网络.
研究的目的:
- 从符号序列中调查隐藏的网络属性的可恢复性.
- 分析代理动态 (随机走路) 和网络拓对重建性能的影响.
- 从顺序数据中确定网络推断的最佳策略.
主要方法:
- 模拟代理在各种网络拓上执行有偏见和无偏见的随机步行.
- 基于访问的节点生成符号序列.
- 评估恢复网络属性的准确性 (例如,度,聚类系数,异心率) 与基本真相相比.
主要成果:
- 重建性能受到代理步行偏差的显著影响.
- 偏向高度邻居通常为大多数网络属性提供最佳性能.
- 集群系数和离心率恢复是例外,在偏向步行时显示的改善较小.
- 自我避开步行提供竞争性表现,特别是在聚类恢复系数方面.
结论:
- 代理动态,特别是步行偏差,在从符号序列推断隐藏的网络结构方面发挥着至关重要的作用.
- 偏向向高度节点的偏向步行对于一般网络属性重建是有效的.
- 像聚类系数这样的特定属性需要替代或补充的方法,例如自我避开步行.
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