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超标伯恩斯坦神经网络:在非欧几里德空间中增强图形卷积
Yanqun Ye1, Xu Chen1, Shuyang Wang2
1Department of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, Ningxia, China.
概括
超标伯恩斯坦神经网络 (HBNN) 通过在超标空间中学习复杂的过器来提高图形神经网络的性能. 这种方法有效地捕捉了层次结构,以便更好地对节点进行分类和链接预测.
科学领域:
- 机器学习 机器学习
- 图形神经网络的神经网络
- 过度波形几何学 过度波形几何学
背景情况:
- 图形卷积神经网络 (GCNs) 嵌入图形数据,但欧几里德嵌入会扭曲权力规律图形中的特征.
- 超标嵌入提供了减少的扭曲,但现有的超标GCN由于简单的过器而难以获得足够的卷积近似.
研究的目的:
- 为改进节点分类和链接预测提出超级伯恩斯坦神经网络 (HBNN).
- 为了将伯恩斯坦多项式扩展到夸张空间,使用梅比乌斯运算来增强过器近似.
- 为了使高阶复杂过器在超标空间中的学习能够实现有效的卷积.
主要方法:
- 在超标空间中,HBNN使用K级的伯恩斯坦多项式近似来估计过器.
- 每个多项式顺序的系数都设置为可学习的参数.
- 梅比乌斯运算被用来将伯恩斯坦多项式扩展到过度方形的多重体.
主要成果:
- HBNN有效地近似复杂的过器在形空间,使更好的卷积.
- 拟议的方法证明了学习图节点的等级结构的能力得到了改善.
- 实验表明,HBNN在节点分类和链接预测任务中的表现优于主流方法.
结论:
- HBNN在超标图形神经网络中提供了显著的进步.
- 这种方法有效地解决了在超标空间中近似卷积的局限性.
- HBNN为分析复杂的图形结构数据提供了一个强大的新工具.
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