论文标题
跨视图在异质图神经网络上通过引导进行自我监督学习
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping
论文作者
论文摘要
异质图神经网络可以代表具有出色能力的异质图的信息。最近，研究了自我监督的学习方式，该学习方式通过对比度学习方法学习了图的独特表达。在没有标签的情况下，这种学习方法表现出巨大的潜力。然而，对比学习在很大程度上取决于正面和负面的对，并且很难从异质图产生高质量的对。在本文中，根据自我监督学习的最新创新，称为BYOL或Boottapping，我们引入了一个可以生成良好表示的而无需产生大量对的表示。此外，请注意以下事实：可以从两个角度查看异质图，网络模式和元路径视图，图中的高级表达式被捕获和表达。所提出的模型比各种现实世界数据集中的其他方法显示出最先进的性能。
Heterogeneous graph neural networks can represent information of heterogeneous graphs with excellent ability. Recently, self-supervised learning manner is researched which learns the unique expression of a graph through a contrastive learning method. In the absence of labels, this learning methods show great potential. However, contrastive learning relies heavily on positive and negative pairs, and generating high-quality pairs from heterogeneous graphs is difficult. In this paper, in line with recent innovations in self-supervised learning called BYOL or bootstrapping, we introduce a that can generate good representations without generating large number of pairs. In addition, paying attention to the fact that heterogeneous graphs can be viewed from two perspectives, network schema and meta-path views, high-level expressions in the graphs are captured and expressed. The proposed model showed state-of-the-art performance than other methods in various real world datasets.
