SGB-Net:可扩展图形宽网络
IEEE transactions on neural networks and learning systems
|April 10, 2025
概括
可扩展图形宽网络 (SGB-Net) 增强了对不断变化的数据的图形表示学习. 它提高了有效性和可扩展性,而不需要重新培训,在基准数据集上表现优于当前的方法.
科学领域:
- 图表学习学习图表学习
- 机器学习是机器学习.
- 数据科学是数据科学.
背景情况:
- 现实世界的图形数据是复杂的,自我发展,对当前的图形学习方法构成挑战.
- 现有的方法与表示学习的局限性作斗争,并需要完全重新培训,以适应不断变化的图形,特别是没有新的标签.
研究的目的:
- 提出一个可扩展的图形宽网络 (SGB-Net),解决当前图形学习方法的局限性.
- 为了增强图形嵌入,并为不断变化的图形数据提供可扩展性.
主要方法:
- 引入了图形特征广泛转换 (GFBT) 层,以扩展图形特征空间并广泛构建模型.
- 开发了两个更新算法:SGB-Net-U用于无标签的增量学习和SGB-Net-S用于标签辅助的增量学习.
- 设计的SGB-Net可以在各种尺度上分别嵌入图形,并适应图形扩展而无需重新训练.
主要成果:
- SGB-Net通过构建可扩展的特征空间来增强图形嵌入.
- SGB-Net-U利用无监督的知识进行无标签的图形增量学习.
- 在传统的增量学习场景中,SGB-Net-S提供了可扩展性.
- 对15个基准数据集的实验表明,SGB-Net在有效性和可扩展性方面超过了最先进的图形神经网络 (GNNs).
结论:
- SGB-Net为图形学习提供了一个可扩展的框架,可以增强表示,并适应不断变化的图形.
- 拟议的GFBT层和更新算法可以在没有完全重新培训的情况下提高性能.
- 与现有的GNN相比,SGB-Net显示出更高的有效性和可扩展性.
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