相关实验视频
猎:特征标签受限制的图形网崩,用于存储效率高的GNN
概括
图形神经网络 (GNN) 是强大的,但内存密集型. 一种新的图形缩小技术FALCON显著缩小了图形大小,同时保留了特征标签分布,从而实现了可扩展的GNN训练.
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:
- 图形神经网络 (GNN) 能够在相互连接的数据上进行机器学习,但由于对大图形的高内存要求,它们面临可扩展性挑战.
- 现有的GNN记忆减少方法往往侧重于推断或忽视特征标签分布,限制其在训练期间的有效性.
- 当前的图形缩小技术很少,并且经常无法解决训练内存的足迹或保存关键数据分布.
研究的目的:
- 介绍FALCON,一种新的拓意识图形缩小技术,旨在克服GNN的内存限制.
- 开发一种方法,在图形缩小过程中保持特征标签分布,确保准确的GNN训练.
- 为了提高可扩展性,将FALCON与现有的内存减少策略集成在一起.
主要方法:
- 猎采用k-means集群与一个新的维度规范化的欧几里德距离来执行拓意识的图形缩小.
- 该方法通过将其纳入图形缩小过程来保持特征标签分布.
- 猎与微分批GNN和量子化技术一起实施和评估.
主要成果:
- 猎显著降低了图形大小,像PPI和Flickr这样的数据集崩至其原始节点的34%.
- 拟议的方法在各种GNN模型中保持了预测质量,证明了其有效性.
- 与最先进的方法相比,基准和废除研究证实了FALCON优越的记忆减少能力.
结论:
- 猎提供了一种有效的解决方案,可以在训练期间减少GNN的内存足迹.
- 该技术成功地保留了基本的特征标签分布,确保了模型在减少后的准确性.
- 猎增强了GNN的可扩展性,用于现实世界的应用程序,使用大规模的图形数据集.
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