通过基于等级 medoid 的对比来提高对抗性攻击的图形对比学习的稳定性
Yawen Shen1, Hui Yang2, Ping Li1
1School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, China; Institute of Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, China.
概括
深度图形Infomax (DGI) 在图形对比学习 (GCL) 中显示出更强的稳定性. 这项研究分析了DGI的分析.
科学领域:
- 图形对比式学习 (GCL)
- 机器学习 机器学习
- 网络分析 网络分析
背景情况:
- 图形对比学习 (GCL) 在图形数据的自我监督学习中表现出色.
- GCL对抗对方攻击的稳定性是一个关键的,有争议的问题.
- 深度图形Infomax (DGI) 显示经验稳定性,促使对其机制进行调查.
研究的目的:
- 从理论上分析DGI的对比机制,以了解其稳定性.
- 根据理论见解开发一个改进的GCL架构.
- 为了增强GCL对抗对方结构攻击的弹性.
主要方法:
- 对DGI的对比机制进行理论分析.
- 开发了一种新的GCL架构,FIRE-GCL (精细粒度与可靠引用形成对比).
- 在不同的图表中对节点分类和链接预测任务的实证验证.
主要成果:
- 通过理论分析确定了DGI稳健性的关键因素.
- FIRE-GCL表现出更好的性能和对抗性强度.
- FIRE-GCL的性能优于最先进的对毒攻击的模型.
结论:
- 阳性样本在保护GCL免受攻击方面发挥着至关重要的作用.
- 拟议的FIRE-GCL架构提供了增强的语义理解和稳定性.
- 结果可以为开发更有弹性的GCL方法提供信息.
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