A curated list of resources for graph prompting methods. Ongoing update.
If some related papers are missing, please contact us via pull requests :)
KDD 2023 (Best Paper Award) - All in One: Multi-Task Prompting for Graph Neural Networks [paper] [code]
CVPR 2023 (Highlight) - Deep Graph Reprogramming [paper]
WWW 2023 - Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer. [paper]
WWW 2023 - GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. [paper] [code]
SIGIR 2023 - Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction. [paper] [code]
Arxiv 2023 - PRODIGY: Enabling In-context Learning Over Graphs. [paper] [code]
Arxiv 2023 - A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges. [paper]
Arxiv 2023 - Universal Prompt Tuning for Graph Neural Networks. [paper]
Arxiv 2023 - SGL-PT: A Strong Graph Learner with Graph Prompt Tuning. [paper]
ArXiv 2023 - CodeKGC: Code Language Model for Generative Knowledge Graph Construction [paper] [code]
ArXiv 2023 - StructGPT: A General Framework for Large Language Model to Reason over Structured Data. [paper] [code]
ArXiv 2023 - PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs. [paper] [code]
AAAI 2022 - Enhanced Story Comprehension for Large Language Models through Dynamic Document-Based Knowledge Graphs. [paper]
KDD 2022 - GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks. [paper]