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Graph Prompt Learning: A Comprehensive Survey and Beyond

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arxiv 2311.16534 v1 pith:2LZ6NPGM submitted 2023-11-28 cs.AI

Xiangguo Sun , Jiawen Zhang , Xixi Wu , Hong Cheng , Yun Xiong , Jia Li This is my paper · ORCID
classification cs.AI
keywords graphdatasurveyapplicationschallengescomprehensiveprogprompt
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Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by \url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and \url{https://github.com/sheldonresearch/ProG}, respectively.

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Forward citations

Cited by 15 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

    cs.LG 2025-10 unverdicted novelty 7.0 of 10

    GILT turns few-shot node, edge, and graph classification into a token-reasoning problem and reaches competitive accuracy on held-out benchmarks with no per-graph tuning and no LLM.

  2. Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?

    cs.LG 2024-12 conditional novelty 7.0 of 10

    Morpher adapts pre-trained GNNs to language using multi-modal prompts and a projector, achieving few-shot, cross-domain, and zero-shot unseen-class classification with weak text supervision.

  3. Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias

    cs.LG 2025-10 conditional novelty 6.0 of 10

    ADPrompt adds per-node feature gating and layer-wise edge message calibration to frozen pre-trained GNNs, reducing attribute and structural bias while keeping node-classification accuracy competitive.

  4. Towards Effective Open-set Graph Class-incremental Learning

    cs.LG 2025-07 conditional novelty 6.0 of 10

    OGCIL replays old-class embeddings generated by a conditional VAE and rejects unknown nodes via prototype-centered hypersphere boundaries, improving open-set graph class-incremental learning on five benchmarks.

  5. EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A frozen-LLM framework with dual-branch token alignment and spatio-temporal prompts beats prior epidemic forecasting models on four COVID-19 datasets.

  6. Prompt-Driven Continual Graph Learning

    cs.LG 2025-02 conditional novelty 6.0 of 10

    PromptCGL learns per-task prompts on a frozen graph neural network and achieves near-joint-training accuracy on four continual graph learning benchmarks with constant memory and near-zero forgetting.

  7. Heterophilic Graph Neural Networks Optimization with Causal Message-passing

    cs.LG 2024-11 conditional novelty 6.0 of 10

    CausalMP rewires a graph using conditional-entropy asymmetry and mutual information, then trains a GNN on the rewired graph to improve link prediction and few-shot node classification.

  8. CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning

    cs.CL 2026-05 conditional novelty 5.0 of 10

    Intermediate LLM thoughts are fed back to rewrite graph token embeddings each step, improving cross-dataset graph-LLM classification and link prediction.

  9. Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding

    cs.LG 2026-02 conditional novelty 5.0 of 10

    GPH^2 pre-trains one expert per graph on edge-dropped or meta-path views and fuses frozen experts with class-wise attention, outperforming type-specific graph pre-training baselines.

  10. GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning

    cs.LG 2025-08 conditional novelty 5.0 of 10

    GSTBench finds that masked feature reconstruction (GraphMAE) is the only one of five graph self-supervised pretraining objectives that consistently transfers across eight datasets, while contrastive methods often perf...

  11. Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    GPAWP prunes low-importance graph prompts using gradient-based importance masks and retunes the remaining prompts, improving few-shot node classification over HGPrompt on ACM, DBLP, and Freebase.

  12. HGMP:Heterogeneous Graph Multi-Task Prompt Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    HGMP combines graph-level contrastive pre-training, type-aware graph augmentation, and per-node-type multiplicative prompts to improve few-shot node, edge, and graph classification on heterogeneous graphs.

  13. GCAL: Adapting Graph Models to Evolving Domain Shifts

    cs.LG 2025-05 conditional novelty 5.0 of 10

    GCAL combines information-maximization adaptation with variational memory graph generation to prevent catastrophic forgetting in unsupervised continual graph domain adaptation.

  14. GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning

    cs.LG 2025-05 conditional novelty 5.0 of 10

    GraphPrompter improves few-shot graph in-context learning by reconstructing prompt subgraphs, selecting prompts with kNN and learned importance, and adding cached pseudo-labeled test samples.

  15. Graph Prompting for Graph Learning Models: Recent Advances and Future Directions

    cs.LG 2025-06 conditional novelty 3.0 of 10

    A survey of graph prompting methods that categorizes them by the stage at which prompts are applied: data, representation, or task.

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