# ProG: A Graph Prompt Learning Benchmark

arXiv: https://arxiv.org/abs/2406.05346 · HTML: https://mlsanity.com/paper/2406.05346/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 72 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 30 of 46)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [GraphMAE: Self-Supervised Masked Graph Autoencoders](https://mlsanity.com/paper/2205.10803/) (2022)
- [InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization](https://mlsanity.com/paper/1908.01000/) (2019)
- [Rethinking Graph Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2205.15508/) (2022)
- [SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation](https://mlsanity.com/paper/2202.03104/) (2022)
- [Training Graph Neural Networks with 1000 Layers](https://mlsanity.com/paper/2106.07476/) (2021)
- [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://mlsanity.com/paper/2302.08043/) (2023)
- [All in One: Multi-task Prompting for Graph Neural Networks](https://mlsanity.com/paper/2307.01504/) (2023)
- [Node Similarity Preserving Graph Convolutional Networks](https://mlsanity.com/paper/2011.09643/) (2020)
- [InfoGCL: Information-Aware Graph Contrastive Learning](https://mlsanity.com/paper/2110.15438/) (2021)
- [Universal Prompt Tuning for Graph Neural Networks](https://mlsanity.com/paper/2209.15240/) (2022)
- [PRODIGY: Enabling In-context Learning Over Graphs](https://mlsanity.com/paper/2305.12600/) (2023)
- [All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining](https://mlsanity.com/paper/2402.09834/) (2024)
- [A Survey of Graph Meets Large Language Model: Progress and Future Directions](https://mlsanity.com/paper/2311.12399/) (2023)
- [ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs](https://mlsanity.com/paper/2402.11235/) (2024)
- [AFEC: Active Forgetting of Negative Transfer in Continual Learning](https://mlsanity.com/paper/2110.12187/) (2021)
- [DeepGCNs: Making GCNs Go as Deep as CNNs](https://mlsanity.com/paper/1910.06849/) (2019)

## Built on by (most cited first, 29 of 29)

- [GLBench: A Comprehensive Benchmark for Graph with Large Language Models](https://mlsanity.com/paper/2407.07457/) (2024)
- [UniGAD: Unifying Multi-level Graph Anomaly Detection](https://mlsanity.com/paper/2411.06427/) (2024)
- [GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning](https://mlsanity.com/paper/2511.05592/) (2025)
- [Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation](https://mlsanity.com/paper/2502.18771/) (2025)
- [One Prompt Fits All: Universal Graph Adaptation for Pretrained Models](https://mlsanity.com/paper/2509.22416/) (2025)
- [Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis](https://mlsanity.com/paper/2410.01635/) (2024)
- [GraphThought: Graph Combinatorial Optimization with Thought Generation](https://mlsanity.com/paper/2502.11607/) (2025)
- [Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs](https://mlsanity.com/paper/2410.12609/) (2024)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [HGMP:Heterogeneous Graph Multi-Task Prompt Learning](https://mlsanity.com/paper/2507.07405/) (2025)
- [Reliable and Compact Graph Fine-tuning via GraphSparse Prompting](https://mlsanity.com/paper/2410.21749/) (2024)
- [Graph Prompting for Graph Learning Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2506.08326/) (2025)
- [Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks](https://mlsanity.com/paper/2410.13974/) (2024)
- [GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning](https://mlsanity.com/paper/2510.04567/) (2025)
- [Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding](https://mlsanity.com/paper/2602.13075/) (2026)
- [When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning](https://mlsanity.com/paper/2601.02662/) (2026)
- [Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models](https://mlsanity.com/paper/2605.13540/) (2026)
- [GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning](https://mlsanity.com/paper/2605.14809/) (2026)
- [Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment](https://mlsanity.com/paper/2607.11374/) (2026)
- [DP-GPL: Differentially Private Graph Prompt Learning](https://mlsanity.com/paper/2503.10544/) (2025)
- [Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting](https://mlsanity.com/paper/2505.16903/) (2025)
- [Aligning the Spectrum: Hybrid Graph Pre-training and Prompt Tuning across Homophily and Heterophily](https://mlsanity.com/paper/2508.11328/) (2025)
- [GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning](https://mlsanity.com/paper/2509.06975/) (2025)
- [GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks](https://mlsanity.com/paper/2602.11629/) (2026)
- [Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs](https://mlsanity.com/paper/2604.16715/) (2026)
- [Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach](https://mlsanity.com/paper/2605.09964/) (2026)
- [Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport](https://mlsanity.com/paper/2609.23547/) (2026)
- [Signed Graph Pre-Training and Prompt Learning](https://mlsanity.com/paper/2609.25722/) (2026)
- [Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting](https://mlsanity.com/paper/2609.32143/) (2026)

## Abstract (arXiv)

Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings. Graph prompt learning emerges as a promising alternative, leveraging lightweight prompts to manipulate data and fill the task gap by reformulating downstream tasks to the pretext. However, several critical challenges still remain: how to unify diverse graph prompt models, how to evaluate the quality of graph prompts, and to improve their usability for practical comparisons and selection. In response to these challenges, we introduce the first comprehensive benchmark for graph prompt learning. Our benchmark integrates SIX pre-training methods and FIVE state-of-the-art graph prompt techniques, evaluated across FIFTEEN diverse datasets to assess performance, flexibility, and efficiency. We also present 'ProG', an easy-to-use open-source library that streamlines the execution of various graph prompt models, facilitating objective evaluations. Additionally, we propose a unified framework that categorizes existing graph prompt methods into two main approaches: prompts as graphs and prompts as tokens. This framework enhances the applicability and comparison of graph prompt techniques. The code is available at: https://github.com/sheldonresearch/ProG.