The paper introduces WAS, a framework that combines weighing and selecting processes to integrate multiple graph pre-training tasks effectively. It addresses the importance and compatibility issues, demonstrating improved performance compared to other methods. Extensive experiments on various datasets validate the effectiveness of WAS in achieving customized task combinations for different instances.
Recent advancements in graph pre-training have led to the development of numerous tasks, highlighting the need for effective integration strategies. The study emphasizes the significance of both task importance and compatibility in achieving optimal performance across diverse datasets. By introducing WAS, the authors showcase a novel approach that outperforms existing methods by addressing these critical factors.
Key findings include:
The results demonstrate that WAS offers a promising solution to the challenges associated with integrating multiple graph pre-training tasks effectively.
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