论文标题
对图表进行预处理的调查：分类法，方法和应用
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
论文作者
论文摘要
诸如BERT之类的验证语言模型（PLM）已彻底改变了自然语言处理（NLP）的景观。受其扩散的启发，巨大的努力已致力于预算的图模型（PGMS）。由于PGM的强大模型架构，可以捕获来自大量标记和未标记的图形数据的丰富知识。模型参数中隐含地编码的知识可以使各种下游任务受益，并有助于减轻图表上学习的几个基本问题。在本文中，我们为PGM提供了首次全面调查。我们首先介绍了图表示学习的局限性，从而引入了图表预训练的动机。然后，我们根据分类学从四个不同的角度根据分类法进行系统地对现有的PGM进行分类。接下来，我们介绍PGM在社会推荐和药物发现中的应用。最后，我们概述了几个有前途的研究方向，这些方向可以作为未来研究的指南。
Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP). Inspired by their proliferation, tremendous efforts have been devoted to Pretrained Graph Models (PGMs). Owing to the powerful model architectures of PGMs, abundant knowledge from massive labeled and unlabeled graph data can be captured. The knowledge implicitly encoded in model parameters can benefit various downstream tasks and help to alleviate several fundamental issues of learning on graphs. In this paper, we provide the first comprehensive survey for PGMs. We firstly present the limitations of graph representation learning and thus introduce the motivation for graph pre-training. Then, we systematically categorize existing PGMs based on a taxonomy from four different perspectives. Next, we present the applications of PGMs in social recommendation and drug discovery. Finally, we outline several promising research directions that can serve as a guideline for future research.
