  Graphs have emerged as a natural choice to represent and analyze the
intricate patterns and rich information of the Web, enabling applications such
as online page classification and social recommendation. The prevailing
"pre-train, fine-tune" paradigm has been widely adopted in graph machine
learning tasks, particularly in scenarios with limited labeled nodes. However,
this approach often exhibits a misalignment between the training objectives of
pretext tasks and those of downstream tasks. This gap can result in the
"negative transfer" problem, wherein the knowledge gained from pre-training
adversely affects performance in the downstream tasks. The surge in
prompt-based learning within Natural Language Processing (NLP) suggests the
potential of adapting a "pre-train, prompt" paradigm to graphs as an
alternative. However, existing graph prompting techniques are tailored to
homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To
bridge this gap, we propose HetGPT, a general post-training prompting framework
to improve the predictive performance of pre-trained heterogeneous graph neural
networks (HGNNs). The key is the design of a novel prompting function that
integrates a virtual class prompt and a heterogeneous feature prompt, with the
aim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPT
introduces a multi-view neighborhood aggregation mechanism, capturing the
complex neighborhood structure in heterogeneous graphs. Extensive experiments
on three benchmark datasets demonstrate HetGPT's capability to enhance the
performance of state-of-the-art HGNNs on semi-supervised node classification.
