通过对合成和现实世界网络中的模型性能进行比较分析,优化用于网络推断的机器学习
Ruby Khan1, Sumbal Khan2, Bakht Pari3
1Department of System Biology and Engineering, Silesian University of Technology, Gliwice, Poland. ruby.khan@polsl.pl.
Scientific reports
|July 8, 2025
概括
在复杂网络分析中,物流回归 (LR) 优于随机森林 (RF),表明更简单的模型可以更有效. 这项研究优化了机器学习,用于在各个领域的网络推理.
科学领域:
- 网络科学 网络科学
- 机器学习 机器学习
- 计算复杂性 计算复杂性
背景情况:
- 复杂的系统和网络分析在社会,生物学和技术领域至关重要.
- 传统的网络模型 (ER,BA,SBM) 难以应对现实世界的网络复杂性,如模块化和无尺度属性.
研究的目的:
- 评估机器学习模型在不同尺度上分析网络结构特征方面的有效性.
- 评估各种用于网络分析的机器学习模型的计算成本.
主要方法:
- 在不同大小 (100,500,1000个节点) 的合成网络上对物流回归 (LR) 和随机森林 (RF) 的比较分析.
- 使用科尔莫戈罗夫-斯米尔诺夫 (K-S) 试验对模块化和巴拉巴西-阿尔伯特 (BA) 模型对无尺度属性的随机区块模型 (SBM) 的评估.
主要成果:
- 在合成网络上,物流回归实现了完美的准确性,精度,回忆,F1分数和AUC,超过了随机森林 (准确率80%).
- SBM有效地模拟了网络模块化,而BA复制了社交网络枢纽结构.
- 像LR这样的更简单的模型在更大,更复杂的网络中展示了优越的概括能力.
结论:
- 简单的机器学习模型可以超过复杂的模型,这取决于网络规模和特征.
- 网络分析的模型选择需要平衡性能与计算权衡.
- 为网络推理优化机器学习对各种科学应用具有广泛的影响.
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