Abstract: In graph mining tasks, prototype-based graph prompt learning has been widely recognized as an effective method to enhance the performance of graph data analysis. However, in few-shot node classification scenarios, existing methods suffer from two key limitations: insufficient utilization of unlabeled data, which leads to inaccurate class prototype construction, and inadequate exploitation of graph topological structure information. These shortcomings restrict the effectiveness of graph prompt learning methods in downstream tasks. To address these issues, this paper proposes a graph prompt learning method that integrates the distribution of all node clusters, named PNCD-GP (prototype with node cluster distribution-graph prompt). This method aims to improve the performance and accuracy of graph data analysis by more effectively leveraging the cluster distribution of unlabeled data and topological structure information. In the pre-training phase, two optimization strategies, predicting masks and preserving graph node clustering, are adopted to learn discriminative node representations and narrow the gap between upstream and downstream tasks. In the graph prompting phase, class prototype virtual nodes are introduced into the original graph as prompts, and high-order information is incorporated to enhance the graph’s topological structure, thereby improving the model’s ability to understand and utilize graph structures. Additionally, prompts are learned by maintaining the cluster distribution between unlabeled samples and labeled nodes. This method enables the construction of more accurate prototype vectors and performs node classification by leveraging the similarity between class prototypes and node representations. Experimental results on multiple public graph datasets demonstrate that the PNCD-GP method exhibits significant advantages in both efficiency and accuracy, verifying its effectiveness and potential in the field of graph prompt learning.

Key words: graph mining, graph prompt learning, graph neural network, few-shot, clustering