GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning

--Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in the community. The key to graph in-context learning is to perform downstream graphs conditioned on chosen prompt examples. Existing methods randomly select subgraphs or edges as prompts, leading to noisy graph prompts and inferior model performance. Additionally, due to the gap between pre-training and testing graphs, when the number of classes in the testing graphs is much greater than that in the training, the in-context learning ability will also significantly deteriorate. T o tackle the aforementioned challenges, we develop a multi-stage adaptive prompt optimization method GraphPrompter, which optimizes the entire process of generating, selecting, and using graph prompts for better in-context learning capabilities. Firstly, Prompt Generator introduces a reconstruction layer to highlight the most informative edges and reduce irrelevant noise for graph prompt construction. Furthermore, in the selection stage, Prompt Selector employs the k -nearest neighbors algorithm and pre-trained selection layers to dynamically choose appropriate samples and minimize the influence of irrelevant prompts. Finally, we leverage a Prompt Augmenter with a cache replacement strategy to enhance the generalization capability of the pre-trained model on new datasets. Extensive experiments show that GraphPrompter effectively enhances the in-context learning ability of graph models. One of the most fascinating properties of Large Language Models (LLMs) is its In-Context Learning capability [1], [2]. It refers to the ability of a pre-trained LLM to achieve competitive results on downstream tasks given only a few prompt examples during the prediction phase, without updating the model weights through fine-tuning approaches. Recently, there have been efforts to transfer this In-Context learning capability from large language models to graph models [3]-[5]. Out of these methods, Prodigy [3] and One For All (OFA) [5] stand out as the most effective frameworks that unify diverse levels of graph-related tasks and achieve competitive in-context learning performance. Generally, the graph in-context learning architecture can be divided into two main parts including data/prompt graph construction and task graph prediction (see Figure 1 as an example for edge classification). Figure 1: Graph In-Context Learning (edge classification as an example) with random prompts selection.