在标签噪音下对比学习图表
Xianxian Li1, Qiyu Li2, De Li2
1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China; Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China.
概括
这项研究引入了对比强大的图形神经网络 (CR-GNN),以解决半监督节点分类中的标签噪声. CR-GNN有效地提高了对杂和稀疏图形数据的性能.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 数据挖掘 数据挖掘
背景情况:
- 半监督节点分类对于图形结构数据至关重要,但性能随着标签噪声和稀疏性而下降.
- 现有的图形神经网络 (GNN) 容易受到标签噪声的影响,特别是当标签数据稀缺时.
研究的目的:
- 提出一种新的方法,即对比强大的图形神经网络 (CR-GNN),以提高在半监督节点分类中对标签噪声的强度.
- 在稀疏和杂标签的场景中提高GNN性能.
主要方法:
- 使用无监督的对比损失和邻近的对比损失,包括图形同性.
- 通过选择可靠的节点,引入动态交叉损失以减轻过度拟合以标记噪声.
- 整合跨空间的一致性来弥合对比和分类任务之间的语义差距.
主要成果:
- 与现有方法相比,CR-GNN在抵抗标签噪声方面表现出优异的性能.
- 在多个公共数据集上进行了广泛的实验,验证了拟议方法的有效性.
结论:
- 在有标签噪声的情况下,CR-GNN为半监督节点分类提供了强大的解决方案.
- 该方法有效地处理稀疏和杂的标签,性能优于当前最先进的技术.
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