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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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多视图对比学习用于图形对抗防御.

Xiao Zhang1, Peng Bao1

  • 1School of Software Engineering, Beijing Jiaotong University, Beijing, 100081, China.

Neural networks : the official journal of the International Neural Network Society
|July 22, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了COLA,这是一种用于图形神经网络 (GNN) 的新型防御框架. 通过多视图对比学习,COLA通过有效利用本地和全球图形信息来增强GNN对对抗攻击的稳定性.

关键词:
敌对的攻击是敌对的攻击.对抗性辩护是对抗性的防御.图形神经网络是一个神经网络.节点的分类 节点的分类坚固性 坚固性 坚固性

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科学领域:

  • 图形神经网络的神经网络
  • 敌对的机器学习
  • 图形表示学习学习学习图形表示学习

背景情况:

  • 图形神经网络 (GNN) 对于图形任务来说很强大,但对对抗性攻击很脆弱.
  • 现有的防御通常专注于单一的观点或有限的信息,忽视了关键的本地和全球图形洞察力.
  • 开发强大的GNN框架是一个重大的研究挑战.

研究的目的:

  • 提出一种新的防御机制,COLA (图形对抗防御的对比学习),以提高GNN的稳定性.
  • 通过结合本地和全球图形信息来解决当前防御方法的局限性.
  • 为了提高节点表示的可靠性,防止对抗性干扰.

主要方法:

  • 使用边缘方向性和图形扩散生成了两个增强图形视图,结合了结构,特征和监督信息.
  • 采用多视图对比学习来通过不同的对比路径编码本地和全球图形信息.
  • 构建了不同的对比路径来导出强大的节点表示.

主要成果:

  • 在七个不同的基准数据集 (四个同性恋,三个异性恋) 上验证了COLA.
  • 证明了COLA在抵抗各种对抗性攻击方面的有效性.
  • 与最先进的基线方法相比,实现了更高的性能.

结论:

  • COLA显著提高了GNN对抗敌对攻击的稳定性.
  • 拟议的多视图对比学习方法有效地利用本地和全球图形信息.
  • 科拉为构建更有弹性的GNN模型提供了一个有希望的方向.