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Graph neural networks have emerged as a powerful tool for graphrepresentation learning, but their performance heavily relies on abundanttask-specific supervision. To reduce labeling requirement, the "pre-train,prompt" paradigms have become increasingly common. However, existing study ofprompting on graphs is limited, lacking a universal treatment to appeal todifferent downstream tasks. In this paper, we propose GraphPrompt, a novelpre-training and prompting framework on graphs. GraphPrompt not only unifiespre-training and downstream tasks into a common task template but also employsa learnable prompt to assist a downstream task in locating the most relevantknowledge from the pre-trained model in a task-specific manner. To furtherenhance GraphPrompt in these two stages, we extend it into GraphPrompt+ withtwo major enhancements. First, we generalize several popular graph pre-trainingtasks beyond simple link prediction to broaden the compatibility with our tasktemplate. Second, we propose a more generalized prompt design that incorporatesa series of prompt vectors within every layer of the pre-trained graph encoder,in order to capitalize on the hierarchical information across different layersbeyond just the readout layer. Finally, we conduct extensive experiments onfive public datasets to evaluate and analyze GraphPrompt and GraphPrompt+.

Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs