可扩展和有效的负样本生成超边缘预测
Shilin Qu1, Weiqing Wang1, Yuan-Fang Li1
1Monash University, Wellington Rd, Melbourne, 3800, Australia.
概括
我们介绍了SEHP,一种创新的方法,用于产生负的超边缘在超图分析. 通过使用条件扩散模型进行超边缘预测,SEHP克服了可扩展性问题并提高了预测准确性.
科学领域:
- 复杂系统分析
- 网络科学
- 机器学习
背景情况:
- 通过捕捉多个实体的交互,超越传统图表来建模复杂的系统.
- 超边缘预测对于分析超图至关重要,需要有效的负超边缘采样来训练模型.
- 现有的负采样方法缺乏通用性和可扩展性,特别是对于大型超图.
研究的目的:
- 开发一种可扩展和有效的方法,用于生成信息化的负超边缘,用于超边缘预测.
- 在超图中适应负超边的离散空间扩散模型.
- 提高超边缘预测模型的准确性和可扩展性.
主要方法:
- 引入了SEHP (可扩展和有效的负样本生成超边缘预测),用于代负超边缘生成和改进的条件扩散模型.
- 开发了分高图采样技术,以整合可扩展的批量培训的全球结构信息.
- 解决了将扩散模型应用于离散负样生成的挑战.
主要成果:
- SEHP有效地产生和完善负的超边缘,将它们推向决策边界以提高模型性能.
- 该方法通过对采样的子超图进行批量训练,证明了卓越的可扩展性.
- 对现实数据集的广泛实验表明,SEHP在预测准确性和可扩展性方面表现优于最先进的方法.
结论:
- SEHP为超图分析带来了显著的进步.
- 拟议的条件扩散模型方法对于大型超图是有效和可扩展的.
- SEHP提高了超边缘预测性能,并解决了现有方法的局限性.
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