论文标题
学习图表以学习图表表示
Learning Graph Augmentations to Learn Graph Representations
论文作者
论文摘要
由于其不规则的结构，急剧的分布变化以及跨数据集的非较高特征空间，设计图形对比度学习的增强是具有挑战性的。我们介绍LG2AR，学习图扩展以学习图表表示，这是一个端到端自动图扩展框架，可帮助编码在节点和图形级别上学习可通用的表示。 LG2AR由一个概率政策组成，该政策可以学习扩大分布和一组概率的增强头，这些概率增强头是通过增强参数学习分布的。我们表明，LG2AR在线性和半监督评估方案中都与以前的无监督模型相比，在20个图形级别和节点级别的基准中，在20个图形和节点级别的基准中实现了最先进的结果。源代码将在此处发布：https：//github.com/kavehhassani/lg2ar
Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Representations, which is an end-to-end automatic graph augmentation framework that helps encoders learn generalizable representations on both node and graph levels. LG2AR consists of a probabilistic policy that learns a distribution over augmentations and a set of probabilistic augmentation heads that learn distributions over augmentation parameters. We show that LG2AR achieves state-of-the-art results on 18 out of 20 graph-level and node-level benchmarks compared to previous unsupervised models under both linear and semi-supervised evaluation protocols. The source code will be released here: https://github.com/kavehhassani/lg2ar
