流诱导的瓦斯斯坦 GCN:通过域调整学习图形嵌入
概括
这项研究介绍了一种强大的图形嵌入方法,即Correntropy诱导的Wasserstein GCN (CW-GCN),以改善噪音图形之间的知识传输. CW-GCN有效地提取干净的信息,并可靠地传输,以便更好地进行图形分析.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 数据挖掘 数据挖掘
背景情况:
- 图形嵌入从复杂的图形结构中学习低维的顶点表示.
- 跨域图形嵌入旨在将学习到的表示从源到目标图形.
- 图表中的噪音对准确的信息提取和知识传输构成重大挑战.
研究的目的:
- 为杂环境开发一个强大的图形嵌入式架构.
- 在交叉图形嵌入任务中提高知识转移的可靠性.
- 为了提高图形分析在图形噪声存在时的性能.
主要方法:
- 建议采用双步电流诱导的瓦斯斯坦GCN (CW-GCN) 架构.
- 第一步利用电流引起的损失来识别和排除源图中的杂节点.
- 第二步使用瓦瑟斯坦距离来调整源和目标图的分布,从而促进知识传输.
主要成果:
- CW-GCN在从清洁节点中提取有用信息方面表现出强大.
- 该方法有效地减轻了知识转移期间噪声的负面影响.
- 广泛的实验表明,CW-GCN在杂的环境中显著超过了最先进的方法.
结论:
- 在噪音的情况下,CW-GCN为交叉图嵌入提供了一个强大的解决方案.
- 拟议的架构促进了可靠的知识传输,以改善图形分析.
- 这种方法为处理杂的图形数据提供了一个有希望的方向.
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