MCGCL:对基于动量梯度候选人的图形对比学习的对抗性攻击.
Qi Zhang1, Zhenkai Qin2, Yunjie Zhang1
1School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China.
PloS one
|June 6, 2024
概括
本研究介绍了一种用于图形对比学习的新型对抗性攻击方法,使用动量梯度来克服局部最佳并提高攻击成功. 新策略提高了融合速度,并优于对基准数据集的现有方法.
科学领域:
- 人工智能的人工智能
- 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:
- 由于离散的图形结构,对图形对比学习的对抗性攻击面临挑战,导致不可靠的梯度和局部最佳值.
- 现有的方法在对抗性图形攻击场景中难以达到梯度准确度和收速度.
研究的目的:
- 提出一种新的对抗性攻击方法,使用动量梯度候选人进行无监督图形对比学习.
- 为了提高结构梯度的可靠性,并在对抗性攻击中克服局部最佳问题.
- 提高在图形表示学习中的对抗性攻击的融合速度和成功率.
主要方法:
- 通过结合先前的梯度信息,将反向传播的梯度转化为动量梯度.
- 导向梯度更新与动量梯度以加快收和提高准确性.
- 根据从两个视图的总动量梯度中得出的突出性来对候选对抗样本进行排名.
主要成果:
- 拟议的势头梯度候选方法与现有的对抗性攻击策略相比,显著提高了趋同速度.
- 三个数据集的实验结果显示,在链接预测任务中表现优异,表现优于监督基线.
- 反对攻击方法在不同的图形表示模型中显示出强大的可转移性.
结论:
- 动量梯度候选方法有效地解决了在对抗性攻击中离散图形结构的局限性.
- 这种方法提供了一种更强大,更有效的策略,用于在图形对比学习中生成对抗样本.
- 证明的可转移性凸显了拟议的攻击技术在图形表示学习中的广泛适用性.
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