A Novel Prompt Tuning for Graph Transformers: Tailoring Prompts to Graph Topologies

Deep graph prompt tuning (DeepGPT), which only tunes a set of continuous prompts for graph transformers, significantly decreases the storage usage during training. However, DeepGPT is limited by its uniform prompts to input graphs with various structures. This is because different graph structures dictate various feature interactions between nodes, while the uniform prompts are not dynamic to tailor the feature transformation for the graph topology. In this paper, we propose a Topo-specific Graph Prompt Tuning (TGPT ), which provides topo-specific prompts tailored to the topological structures of input graphs. Specifically, TGPT learns trainable embeddings for graphlets and frequencies, where graphlets are fundamental sub-graphs that describe the structure around specific nodes. Based on the statistic data about graphlets of input graph, topo-specific prompts are generated by graphlet embeddings and frequency embeddings. The topo-specific prompts include node-level topo-specific prompts for specified nodes, a graph-level topo-specific prompt for the entire graph, and a task-specific prompt to learn task-related information. They are all inserted into specific graph nodes to perform feature transformation, providing specified feature transformation for input graphs with different topological structures. Extensive experiments show that our method outperforms existing lightweight fine-tuning methods and DeepGPT in molecular graph classification and regression with comparable parameters.