走向强大的图形 半监督学习 针对极端的数据稀缺性
IEEE transactions on neural networks and learning systems
|February 29, 2024
概括
增强图形自我训练 (AGST) 提高了图形神经网络的性能,使用有限的标记数据. 这种新的框架通过结合结构和语义数据增强来提高节点的稳定性,以更好地分类节点.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 图形分析分析 图形分析
背景情况:
- 图形神经网络 (GNN) 需要大量的人类注释数据来进行网络挖掘.
- 在图表上进行半监督学习是具有挑战性的,因为在特征标签传播和决策边界学习方面存在困难,标记节点很少.
研究的目的:
- 为低数据场景开发一个强大的图形预测模型.
- 通过捕获结构和语义知识来解决对图形结构数据的自我训练的局限性.
主要方法:
- 提出了一个新的图形数据增强框架,称为增强图形自训练 (AGST).
- AGST包含两个新的增强模块:结构和语义.
- 使用了解的图形自训练 (GST) 骨干.
主要成果:
- 对半监督节点分类任务进行了全面的评估.
- 该框架在各种有限的标记节点数据场景下进行了测试.
- 实验结果表明,在数据不足的情况下,框架的有效性.
结论:
- 拟议的AGST框架显著提高了带有有限标记数据的图形预测模型的稳定性.
- 新型数据增强模块为节点分类性能做出了独特的贡献.
- AGST为可持续的图形半监督学习提供了一个有希望的解决方案.
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