  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.
