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Graphs have emerged as a natural choice to represent and analyze theintricate patterns and rich information of the Web, enabling applications suchas online page classification and social recommendation. The prevailing"pre-train, fine-tune" paradigm has been widely adopted in graph machinelearning tasks, particularly in scenarios with limited labeled nodes. However,this approach often exhibits a misalignment between the training objectives ofpretext tasks and those of downstream tasks. This gap can result in the"negative transfer" problem, wherein the knowledge gained from pre-trainingadversely affects performance in the downstream tasks. The surge inprompt-based learning within Natural Language Processing (NLP) suggests thepotential of adapting a "pre-train, prompt" paradigm to graphs as analternative. However, existing graph prompting techniques are tailored tohomogeneous graphs, neglecting the inherent heterogeneity of Web graphs. Tobridge this gap, we propose HetGPT, a general post-training prompting frameworkto improve the predictive performance of pre-trained heterogeneous graph neuralnetworks (HGNNs). The key is the design of a novel prompting function thatintegrates a virtual class prompt and a heterogeneous feature prompt, with theaim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPTintroduces a multi-view neighborhood aggregation mechanism, capturing thecomplex neighborhood structure in heterogeneous graphs. Extensive experimentson three benchmark datasets demonstrate HetGPT's capability to enhance theperformance of state-of-the-art HGNNs on semi-supervised node classification.

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks