Key Results

Superior Performance

The proposed method outperforms existing methods across a variety of graph tasks. This includes node classification, graph classification, and link prediction tasks. The results indicate that the proposed method can effectively generalize across different tasks and improve the performance of GNNs.

Effectiveness of Meta-learning

The results show that the introduction of meta-learning significantly improves the performance of the proposed method. This suggests that meta-learning can effectively learn better prompts for multi-task settings.

Robustness and Flexibility

The proposed method demonstrates robustness and flexibility across different graph structures and sizes. This indicates that the method can adapt to various graph tasks and is not limited by the specific characteristics of the graphs.

Efficiency of Learning

The learning process of the proposed method is efficient. The authors show that the method can quickly converge to a good solution, which suggests that it can be applied to large-scale graph tasks. These results provide strong evidence for the effectiveness of the proposed method and its potential for improving the performance of GNNs in various tasks.