  In recent years, prompt tuning has sparked a research surge in adapting
pre-trained models. Unlike the unified pre-training strategy employed in the
language field, the graph field exhibits diverse pre-training strategies,
posing challenges in designing appropriate prompt-based tuning methods for
graph neural networks. While some pioneering work has devised specialized
prompting functions for models that employ edge prediction as their
pre-training tasks, these methods are limited to specific pre-trained GNN
models and lack broader applicability. In this paper, we introduce a universal
prompt-based tuning method called Graph Prompt Feature (GPF) for pre-trained
GNN models under any pre-training strategy. GPF operates on the input graph's
feature space and can theoretically achieve an equivalent effect to any form of
prompting function. Consequently, we no longer need to illustrate the prompting
function corresponding to each pre-training strategy explicitly. Instead, we
employ GPF to obtain the prompted graph for the downstream task in an adaptive
manner. We provide rigorous derivations to demonstrate the universality of GPF
and make guarantee of its effectiveness. The experimental results under various
pre-training strategies indicate that our method performs better than
fine-tuning, with an average improvement of about 1.4% in full-shot scenarios
and about 3.2% in few-shot scenarios. Moreover, our method significantly
outperforms existing specialized prompt-based tuning methods when applied to
models utilizing the pre-training strategy they specialize in. These numerous
advantages position our method as a compelling alternative to fine-tuning for
downstream adaptations.
