  The text-attributed graph (TAG) is one kind of important real-world
graph-structured data with each node associated with raw texts. For TAGs,
traditional few-shot node classification methods directly conduct training on
the pre-processed node features and do not consider the raw texts. The
performance is highly dependent on the choice of the feature pre-processing
method. In this paper, we propose P2TAG, a framework designed for few-shot node
classification on TAGs with graph pre-training and prompting. P2TAG first
pre-trains the language model (LM) and graph neural network (GNN) on TAGs with
self-supervised loss. To fully utilize the ability of language models, we adapt
the masked language modeling objective for our framework. The pre-trained model
is then used for the few-shot node classification with a mixed prompt method,
which simultaneously considers both text and graph information. We conduct
experiments on six real-world TAGs, including paper citation networks and
product co-purchasing networks. Experimental results demonstrate that our
proposed framework outperforms existing graph few-shot learning methods on
these datasets with +18.98% ~ +35.98% improvements.
