The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP). Initial graph prompt tuning approaches tailored specialized prompting functions for Graph Neural Network (GNN) models pre-trained with specific strategies, such as edge prediction, thus limiting their applicability. In contrast, another pioneering line of research has explored universal prompting via adding prompts to the input graph's feature space, thereby removing the reliance on specific pre-training strategies. However, the necessity to add feature prompts to all nodes remains an open question. Motivated by findings from prompt tuning research in the NLP domain, which suggest that highly capable pre-trained models need less conditioning signal to achieve desired behaviors, we advocate for strategically incorporating necessary and lightweight feature prompts to certain graph nodes to enhance downstream task performance. This introduces a combinatorial optimization problem, requiring a policy to decide 1) which nodes to prompt and 2) what specific feature prompts to attach. We then address the problem by framing the prompt incorporation process as a sequential decision-making problem and propose our method, RELIEF, which employs Reinforcement Learning (RL) to optimize it. At each step, the RL agent selects a node (discrete action) and determines the prompt content (continuous action), aiming to maximize cumulative performance gain. Extensive experiments on graph and node-level tasks with various pre-training strategies in few-shot scenarios demonstrate that our RELIEF outperforms fine-tuning and other prompt-based approaches in classification performance and data efficiency.

翻译："预训练-提示"范式继在自然语言处理领域取得成就后，近期已将其泛化能力和数据效率扩展至图表示学习领域。早期的图提示调优方法为采用特定策略（如边预测）预训练的图神经网络模型定制了专门的提示函数，从而限制了其适用性。相比之下，另一开创性研究方向探索了通过在图输入的特征空间添加提示来实现通用提示，从而摆脱了对特定预训练策略的依赖。然而，是否需要对所有节点添加特征提示仍是一个开放性问题。受自然语言处理领域提示调优研究的启发——该研究表明能力强大的预训练模型需要更少的条件信号即可实现期望行为——我们主张策略性地将必要且轻量的特征提示融入特定图节点，以提升下游任务性能。这引出了一个组合优化问题，需要制定策略以决定：1）对哪些节点添加提示；2）添加何种具体特征提示。为此，我们将提示融入过程建模为序列决策问题，并提出RELIEF方法，该方法采用强化学习进行优化。在每一步中，强化学习智能体选择节点（离散动作）并确定提示内容（连续动作），以最大化累积性能增益。在少样本场景下，针对采用不同预训练策略的图级与节点级任务进行的广泛实验表明，RELIEF在分类性能和数据效率方面均优于微调及其他基于提示的方法。