提示GCN:在轻量级GCN中弥合子图的差距
IEEE transactions on neural networks and learning systems
|January 28, 2026
概括
PromptGCN推出了一种新的基于提示的图形卷积网络 (GCN),用于解决大规模图形分析中的内存限制. 这种轻量级模型通过使用全球信息有效弥合子图的差距来提高准确性.
科学领域:
- 机器学习 机器学习
- 图形神经网络 图形神经网络
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 图形卷积网络 (GCNs) 在大型图形上面临内存限制,导致内存外错误.
- 亚图采样方法减少了内存使用,但造成了信息差距,降低了GCN的准确性.
- 现有的轻量级GCN正在努力有效地利用全球图形背景.
研究的目的:
- 提出一个新的基于提示的轻量级GCN模型PromptGCN.
- 为了弥合分图采样方法中固有的信息差距.
- 在大型图表上提高GCN的准确性和效率.
主要方法:
- 引入可学习的提示嵌入,以捕获全球图形信息.
- 在每个子图中附加提示,以促进全球信息传输.
- 集成式基于提示的学习,使用分图采样技术.
主要成果:
- 在七个大规模图形数据集中,PromptGCN表现出卓越的性能.
- 与基线方法相比,在Flickr数据集上实现了高达5.48%的精度改进.
- 有效地减少了内存消耗,同时保持或提高了准确性.
结论:
- PromptGCN成功地弥合了子图信息缺口,提高了GCN的准确性.
- 该模型很容易与现有的子图采样方法集成.
- PromptGCN为高效准确的大规模图形分析提供了一个可行的解决方案.
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