A Unified Prompt for Enhancing Heterogeneous Graph Pre-training via Edge-based Message Passing

Abstract

Inspired by natural language processing prompt learning, recent heterogeneous graph prompt-tuning methods have been developed to better align pre-trained models with downstream tasks. However, existing heterogeneous prompt methods primarily focus on holistic framework design, causing prompts to heavily depend on specific pre-trained models and thus limiting generalization. To address this, we focus on the common encoder module shared across pre-trained models and the multi-relational edge structure unique to heterogeneous graphs, proposing a novel heterogeneous graph prompt-tuning method named HGMRP. It simultaneously resolves the issue of inconsistent objectives during prompt optimization when messages propagate across different relational edges. By assigning learnable prompt vectors to different types of edges, HGMRP integrates multi-relational prompts directly into the message-passing process. Furthermore, we extend single-relation prompts into a composite structure that includes both relation-specific and shared components, thereby enhancing expressive capability. Extensive experiments conducted on four widely used heterogeneous graph datasets show that HGMRP can adapt to various types of pre-trained models and significantly outperforms existing heterogeneous prompt methods, validating its effectiveness and superiority.