Task-Specific Prompt with Global Context for Multi-Task Graph Pre-Training
Graph prompt learning is an effective paradigm to adapt pre-trained graph models to downstream tasks in low-resource scenarios. However, existing multi-task graph pre-training frameworks generally use randomly initialized prompts, leading to poor alignment between the prompt space, pretext objectives and graph structural characteristics. This greatly weakens the task relevance, structural awareness and transferability of prompt representations. To address this challenge, we propose TPGC, a dual-prior prompt initialization solution that explicitly models the synergy between task prior and structural prior. Specifically, the Task-Prior Injection Module first conducts a short homologous mult...
| Authors | Zhiyang Qiu, Yangtao Wang, Xiaocui Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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