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一个全面的解决方案框架空间探索在社区检测检测
Fabio Morea1, Domenico De Stefano2
1Area Science Park, Padriciano 99, Trieste, Italy.
Scientific reports
|November 1, 2025
概括
在社区检测中,通过系统地探索算法输出来提高可重复性. 一个新的贝叶斯框架和解决方案空间分类学提高了网络分析的可靠性和解释性.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 数据挖掘是一种数据挖掘.
背景情况:
- 社区检测算法对于网络分析至关重要.
- 由于节点顺序和异常值,算法结果缺乏可重现性.
- 目前的方法阻碍了对复杂网络的可靠解释.
研究的目的:
- 引入一个系统探索社区检测算法解决方案空间的框架.
- 提高网络社区检测的可复制性和可解释性.
- 开发一种方法来评估分区可靠性和指导算法选择.
主要方法:
- 重复运行社区检测算法,并使用交换节点命令.
- 采用贝叶斯模型来评估解决方案的融合和估计概率.
- 开发用于诊断分析的解决方案空间分类法.
- 将框架应用于现实世界的网络数据集.
主要成果:
- 拟议的框架系统地探索社区检测算法的解决方案空间.
- 贝叶斯模型提供了一个强大的停止规则,平衡精度和计算成本.
- 解决方案空间分类学为分区可靠性提供了明确的诊断.
- 不同的算法表现出不同的解决方案空间特征.
结论:
- 系统地探索解决方案空间对于可靠的网络分析至关重要.
- 贝叶斯框架和解决方案空间分类学提高了可重现性和解释性.
- 了解跨不同解决方案空间的算法行为有助于选择合适的方法.
- 这种方法可以从网络数据中得出更容易辩护的科学结论.
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