ERMAV:高效和强大的图形通过多对手观点对比学习培训培训
IEEE transactions on cybernetics
|March 20, 2025
概括
本研究介绍了ERMAV,一个高效和强大的图形对比学习 (GCL) 框架. 通过使用多对手视图,ERMAV增强了GCL对对手攻击的弹性,提高了攻击图的性能.
科学领域:
- 图形表示学习学习学习图形表示学习
- 机器学习安全 机器学习安全
- 人工智能的人工智能
背景情况:
- 图形对比学习 (GCL) 对于图形表示学习至关重要.
- 现有的GCL方法容易受到对抗性攻击.
- 目前强大的GCL方法在计算上昂贵,缺乏可扩展性.
研究的目的:
- 提出一个高效和强大的GCL框架来抵御对抗性攻击.
- 解决现有的强大的GCL方法的低效率和可扩展性问题.
主要方法:
- 引入了ERMAV (通过多对手观点培训提供高效和强大的GCL).
- 通过攻击节点属性和子图上的潜在表示来生成对抗性视图.
- 采用高效的攻击方法来产生动态的敌对干扰.
主要成果:
- 在原始图表上,ERMAV的性能优于最先进的GCL方法.
- 与攻击图表上的现有方法相比,ERMAV表现出优越的稳定性.
- 在七个现实世界数据集上进行了广泛的实验,验证了框架的有效性.
结论:
- 对于强大的 GCL,ERMAV 提供了一种高效且可扩展的解决方案.
- 拟议的多对手观点培训增强了GCL的弹性.
- 对于需要强大的图形表示的现实世界应用程序,ERMAV显示出巨大的潜力.
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