mlsanityData to 7 Oct 2026

Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning

arXiv 2410.07074 ↗First posted 9 Oct 2024cs.LGcited by 21

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Abstract (arXiv)

Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. We introduce AskGNN, a novel approach that bridges this gap by leveraging In-Context Learning (ICL) to integrate graph data and task-specific information into LLMs. AskGNN employs a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. Our learning-to-retrieve algorithm optimizes the retriever to select example nodes that maximize LLM performance on graph. Experiments across three tasks and seven LLMs demonstrate AskGNN's superior effectiveness in graph task performance, opening new avenues for applying LLMs to graph-structured data without extensive fine-tuning.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

36 of 53 references.

References · 36

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2020Scaling Laws for Neural Language Modelscited by 6,411
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  8. 2023Qwen Technical Reportcited by 3,273
  9. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  10. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  11. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  12. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
24 more
  1. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  2. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 915
  3. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  4. 2022A Survey on In-context Learningcited by 717
  5. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
  6. 2023A Survey on Large Language Models for Recommendationcited by 378
  7. 2024Long-context LLMs Struggle with Long In-context Learningcited by 285
  8. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  9. 2023Language is All a Graph Needscited by 219
  10. 2023Inverse Scaling: When Bigger Isn't Bettercited by 205
  11. 2024LLaGA: Large Language and Graph Assistantcited by 177
  12. 2023Unified Demonstration Retriever for In-Context Learningcited by 141
  13. 2024A Tale of Tails: Model Collapse as a Change of Scaling Lawscited by 127
  14. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 116
  15. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  16. 2023PRODIGY: Enabling In-context Learning Over Graphscited by 97
  17. 2020Be More with Less: Hypergraph Attention Networks for Inductive Text Classificationcited by 73
  18. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  19. 2024InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignmentcited by 56
  20. 2023OpenICL: An Open-Source Framework for In-context Learningcited by 51
  21. 2023HYTREL: Hypergraph-enhanced Tabular Data Representation Learningcited by 35
  22. 2023GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphscited by 28
  23. 2024Empowering Large Language Models for Textual Data Augmentationcited by 17
  24. 2024MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learningcited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 12.

21 papers.

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  1. 2025The Hallucination Tax of Reinforcement Finetuningcited by 35
  2. 2025GVPO: Group Variance Policy Optimization for Large Language Model Post-Trainingcited by 32
  3. 2025GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Designcited by 21
  4. 2026Graph is a Substrate Across Data Modalitiescited by 13
  5. 2026GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learningcited by 7
  6. 2025From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Contextcited by 6
  7. 2025Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applicationscited by 5
  8. 2026GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedbackcited by 4
  9. 2026Higher-Order Knowledge Representations for Agentic Scientific Reasoningcited by 3
  10. 2026CoAct: Co-Active LLM Preference Learning with Human-AI Synergycited by 3
  11. 2025GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learningcited by 2
  12. 2026Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphscited by 2
9 more
  1. 2025Graph-Based Alternatives to LLMs for Human Simulationcited by 1
  2. 2026When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selectioncited by 1
  3. 2025Graph-Guided Dual-Level Augmentation for 3D Scene Segmentationcited by 0
  4. 2025Text2Graph: Combining Lightweight LLMs and GNNs for Efficient Text Classification in Label-Scarce Scenarioscited by 0
  5. 2026Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teachingcited by 0
  6. 2026Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoningcited by 0
  7. 2026Adaptive Recurrent Message Passing for Test Time Computing on Graphscited by 0
  8. 2026Trojaning the Alignment: Stealthy Backdoor Attacks against Graph Foundation Modelscited by 0
  9. 2026LoGIC: Budgeted Context Construction for Node-Level Graph In-Context Learning with Tabular Foundation Modelscited by 0