标记为平衡:在节点分类上的图形神经网络的性能提升器
Yi Luo1, Guangchun Luo1, Guiduo Duan1
1University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-tech Zone (West District), Chengdu, 611731, Sichuan, China.
概括
本研究介绍了标签作为平衡 (LaE),这是一个新的图形神经网络 (GNN) 标签重复使用方法. 通过防止过拟合和优化内存使用,LaE提高了节点分类的准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图表采矿 图表采矿
背景情况:
- 图形神经网络 (GNN) 对于节点分类任务至关重要.
- 标签重复使用方法提高了GNN的性能,但由于过度装配和高内存消耗而受到影响.
- 现有的标签重复使用技术在一般化和计算效率方面存在局限性.
研究的目的:
- 引入标签作为平衡点 (LaE),用于图形神经网络的先进标签重复使用方法.
- 在当前的标签重复使用策略中解决过和内存低效的问题.
- 为了提高基于GNN的节点分类的准确性和通用性.
主要方法:
- 开发了Label as Equilibrium (LaE) 标签,具有改进的掩盖策略和监督隐藏.
- 实现了一种在恒定内存中可优化无限次代的技术.
- 在八个现实数据集上进行了广泛的节点分类实验.
主要成果:
- 劳埃显著提高了当前GNN的准确性得分,平均为2.31%.
- 在数据集中,LaE的表现平均比以前的标签重复使用方法高出1.60%.
- 提出的方法在节点分类任务中表现出卓越的性能和效率.
结论:
- 标签作为平衡 (LaE) 有效地解决了在GNN标签重复使用中的过和内存消耗问题.
- 在节点分类准确性和通用性方面,LaE提供了显著的改进.
- 这种LaE方法具有广泛的适用性,可以使许多最先进的GNN模型受益.
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