Qianhua Tang — accepted AI-conference papers

2026

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

Fengyu Yan, Xiaobao Wang, Qianhua Tang, Dongxiao He, Di Jin

IJCAI 2026

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 heav

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2026

Structure-Enhanced Adapter for Self-Supervised Heterogeneous Graph Learning

Fengyu Yan, Di Jin, Xiaobao Wang, Qianhua Tang, Dongxiao He

AAAI 2026technical

Real-world heterogeneous data is commonly modeled as heterogeneous information networks (HINs). Building upon advancements in graph neural networks (GNNs), existing research has significantly progressed in semi-supervised and self-supervised paradigms for heterogeneous GNNs (HGNNs). However, these m

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