mlsanityData to 14 Oct 2026

Graph Prompt Learning: A Comprehensive Survey and Beyond

arXiv 2311.16534 ↗First posted 28 Nov 2023cs.AIcited by 61

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Abstract (arXiv)

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 urlhttps://github.com/WxxShirley/Awesome-Graph-Prompt, and urlhttps://github.com/sheldonresearch/ProG, respectively.

Key facts. Cited by 61 papers in the map (data through 14 Oct 2026). Builds on 97 of its 127 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

97 of 127 references.

References · 97

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  8. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  9. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
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  11. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
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38 more
  1. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  2. 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,471
  3. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  4. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  5. 2018Deep Graph Infomaxcited by 1,106
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  15. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  16. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  17. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
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50 most cited of 97 All 97 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 29; 2025: 22; 2026: 10.

61 papers.

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  1. 2024All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretrainingcited by 86
  2. 2024ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphscited by 85
  3. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  4. 2024Non-Homophilic Graph Pre-Training and Prompt Learningcited by 31
  5. 2024ProG: A Graph Prompt Learning Benchmarkcited by 29
  6. 2024A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoningcited by 25
  7. 2024RAGraph: A General Retrieval-Augmented Graph Learning Frameworkcited by 24
  8. 2024Cross-Context Backdoor Attacks against Graph Prompt Learningcited by 14
  9. 2024Prompt Learning on Temporal Interaction Graphscited by 13
  10. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  11. 2024GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasetscited by 12
  12. 2024Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approachcited by 12
38 more
  1. 2025One Prompt Fits All: Universal Graph Adaptation for Pretrained Modelscited by 11
  2. 2024Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysiscited by 10
  3. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  4. 2024Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Levelcited by 9
  5. 2024RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuningcited by 9
  6. 2024Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Taskscited by 8
  7. 2024Urban Region Pre-training and Prompting: A Graph-based Approachcited by 8
  8. 2025GCAL: Adapting Graph Models to Evolving Domain Shiftscited by 8
  9. 2024Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphscited by 7
  10. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 6
  11. 2025GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learningcited by 6
  12. 2024Exploring Prompt Engineering: A Systematic Review with SWOT Analysiscited by 5
  13. 2024Heterophilic Graph Neural Networks Optimization with Causal Message-passingcited by 4
  14. 2025HGMP:Heterogeneous Graph Multi-Task Prompt Learningcited by 4
  15. 2024All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)cited by 3
  16. 2024Reliable and Compact Graph Fine-tuning via GraphSparse Promptingcited by 3
  17. 2025A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorderscited by 3
  18. 2025Graph Prompting for Graph Learning Models: Recent Advances and Future Directionscited by 3
  19. 2024A Unified Graph Selective Prompt Learning for Graph Neural Networkscited by 2
  20. 2024Krait: A Backdoor Attack Against Graph Prompt Tuningcited by 2
  21. 2024Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networkscited by 2
  22. 2025A Survey of Cross-domain Graph Learning: Progress and Future Directionscited by 2
  23. 2025GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learningcited by 2
  24. 2025Beyond Single-Granularity Prompts: A Multi-Scale Chain-of-Thought Prompt Learning for Graphcited by 2
  25. 2026Data-centric Prompt Tuning for Dynamic Graphscited by 2
  26. 2026Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encodingcited by 2
  27. 2024Towards Graph Prompt Learning: A Survey and Beyondcited by 1
  28. 2024Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learningcited by 1
  29. 2025Prompt-Driven Continual Graph Learningcited by 1
  30. 2025AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Modelscited by 1
  31. 2025Heterogeneous Graph Prompt Learning via Adaptive Weight Pruningcited by 1
  32. 2025SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLMcited by 1
  33. 2026CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Expertscited by 1
  34. 2026PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphscited by 1
  35. 2025Rethinking Link Prediction for Directed Graphscited by 0
  36. 2025DP-GPL: Differentially Private Graph Prompt Learningcited by 0
  37. 2025Pre-trained Prompt-driven Semi-supervised Local Community Detectioncited by 0
  38. 2025PLACE: Prompt Learning for Attributed Community Search in Large Graphscited by 0

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