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重新思考噪音标签在图形分类中的影响:从实用性和隐私的角度来看
De Li1, Xianxian Li1, Zeming Gan2
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 & Security, Guangxi Normal University, Guilin, 541004, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, China.
概括
图形神经网络 (GNN) 中的噪音标签会损害模型性能和隐私. 我们强大的图形神经网络 (RGLC) 方法有效地纠正噪音标签,增强实用性并保护图形分类中的数据隐私.
科学领域:
- * 机器学习 * 机器学习
- * 图表神经网络的神经网络
- * 数据隐私 数据隐私
背景情况:
- * 图形神经网络 (GNN) 在图形分类方面表现出色,但在杂的标签上扎.
- *现有的噪音标签方法主要针对视觉数据或节点分类,往往忽视隐私影响.
- * 噪音标签影响模型实用性和数据隐私,特别是在图形分类中.
研究的目的:
- * 调查噪音标签对图形分类的双重影响:模型实用性和数据隐私.
- * 提出一种新的强大的图形神经网络 (RGLC) 方法,用于处理图形分类中的噪音标签.
- * 在标签噪声存在的情况下,提高GNN的性能和隐私保护能力.
主要方法:
- * 开发了一个强大的图形神经网络 (RGLC),采用高可靠性样本和主要组件向量进行样本过.
- * 实现了基于双空间信息指导的噪声标签校正,使用强大的主要组件向量和数据增强.
- * 综合监督图形对比学习以提高嵌入质量和保护培训数据隐私.
主要成果:
- * RGLC方法在八个现实世界图形分类数据集中显示出显著的性能增长,在30%的噪音标签率下,改进率从0.8%到7.8%不等.
- * 提出的方法有效地减轻了噪音标签对模型概括的影响.
- * RGLC成功地降低了会员推断攻击的准确性,保持在60%以下,从而提高了数据隐私.
结论:
- * 图形分类中的噪音标签降低了模型的实用性,并通过增加对会员推断攻击的脆弱性来损害数据隐私.
- * 拟议的RGLC方法为噪音标记图形分类提供了一个强大的解决方案,同时提高模型性能和数据隐私.
- * RGLC在处理杂图形数据方面取得了重大进展,在实用性和隐私保护方面都超过了现有的最先进方法.
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