隐含的模型,潜在的压缩,内在的偏见,以及在社区检测中廉价的午餐
Tiago P Peixoto1, Alec Kirkley2
1Department of Network and Data Science, Central European University, 1100 Vienna, Austria.
Physical review. E
|September 19, 2023
概括
我们开发了一个统一的框架,通过将目标与生成模型联系起来来比较社区检测算法. 这种方法可以在没有基本真相标签的情况下进行原则性性能比较,揭示算法偏差并改进网络分析.
科学领域:
- 网络科学 网络科学
- 数据挖掘是一种数据挖掘.
- 计算社会科学 计算社会科学
背景情况:
- 社区检测算法将网络划分为集群,以揭示结构.
- 现有的方法有不同的目标 (推理与描述),阻碍了直接比较.
- 需要一个统一的框架来客观地评估和比较这些算法.
研究的目的:
- 引入一种基于原则的方法来比较各种社区检测算法.
- 将任何社区检测目标与隐性网络生成模型联系起来.
- 为算法性能提供衡量标准,而不依赖于基准真相标签.
主要方法:
- 将社区检测目标与隐性网络生成模型联系起来.
- 在任意目标下计算网络和分区的描述长度.
- 分析算法偏差和过拟合趋势.
- 在人工和实证网络数据集上比较算法.
主要成果:
- 该框架允许推断和描述性社区检测方法的原则性比较.
- 更具表现力的方法在结构化网络数据上显示出优越的压缩性能.
- 这种方法揭示了描述性方法的内在偏见和过度拟合.
- 在各种结构化网络实例中,性能始终优越.
结论:
- 拟议的框架提供了一种统一的方法来评估和比较社区检测算法.
- 表达式算法在结构化数据上优于专门的算法,在这种情况下挑战了"没有免费午餐"定理.
- 这项工作为推进网络分析和理解复杂系统提供了一个强大的工具.
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