找到核心标签,以最大限度地概括图形神经网络
Sichao Fu1, Xueqi Ma2, Yibing Zhan3
1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
概括
我们介绍了图形神经网络 (GNN) 的子集假设,确定了用于训练的核心数据子集. 在这个核心子集上训练GNN可以提高图表表示学习性能.
科学领域:
- 机器学习 机器学习
- 图形表示学习学习学习图形表示学习
背景情况:
- 图形神经网络 (GNN) 广泛用于半监督图形表示学习.
- 研究主要集中在GNN方法学上,对训练数据选择的重视较小.
- 高质量的培训数据对于有效的半监督学习至关重要.
研究的目的:
- 为解决在节点分类中为GNN选择训练数据的问题.
- 识别优化GNN性能的代表性节点子集.
- 提出和验证一个新的假设来选择图形学习中最佳的训练数据.
主要方法:
- 对图形数据的"子集假说"的介绍和阐述,类似于彩票假说.
- 开发一种高效的算法,以在图中识别核心数据子集.
- 在各种数据集和GNN架构中进行实验验证.
主要成果:
- 拟议的子集假设假设存在一个核心数据子集,该子集捕获了基本数据集属性.
- 开发的算法有效地识别了GNN培训的核心子集.
- 在选定的核心子集上接受培训的GNN始终表现出性能改进.
结论:
- 培训数据的质量和选择显著影响了GNN的性能.
- 子集假设为理解图形学习中的最佳数据选择提供了一个新的框架.
- 拟议的方法提供了一种有效的方法,通过战略数据选择来增强GNN.
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