mlsanityData to 7 Oct 2026

LLaGA: Large Language and Graph Assistant

arXiv 2402.08170 ↗First posted 13 Feb 2024cs.LG cs.AIcited by 177

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Abstract (arXiv)

Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in deep learning. However, their application to graph data poses distinct challenges due to the inherent difficulty of translating graph structures to language. To this end, we introduce the Large Language and Graph Assistant (LLaGA), an innovative model that effectively integrates LLM capabilities to handle the complexities of graph-structured data. LLaGA retains the general-purpose nature of LLMs while adapting graph data into a format compatible with LLM input. LLaGA achieves this by reorganizing graph nodes to structure-aware sequences and then mapping these into the token embedding space through a versatile projector. LLaGA excels in versatility, generalizability and interpretability, allowing it to perform consistently well across different datasets and tasks, extend its ability to unseen datasets or tasks, and provide explanations for graphs. Our extensive experiments across popular graph benchmarks show that LLaGA delivers outstanding performance across four datasets and three tasks using one single model, surpassing state-of-the-art graph models in both supervised and zero-shot scenarios. Our code is available at urlhttps://github.com/VITA-Group/LLaGA.

Key facts. Cited by 177 papers in the map (data through 7 Oct 2026). Builds on 37 of its 41 references that are in the map. First posted on arXiv 13 Feb 2024.

Builds on

37 of 41 references.

References · 37

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2017Graph Attention Networkscited by 8,854
  7. 2023Visual Instruction Tuningcited by 7,792
  8. 2017Inductive Representation Learning on Large Graphscited by 6,284
  9. 2018How Powerful are Graph Neural Networks?cited by 4,355
  10. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  11. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  12. 2019Simplifying Graph Convolutional Networkscited by 1,470
25 more
  1. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  2. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
  3. 2020A Generalization of Transformer Networks to Graphscited by 653
  4. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  5. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  6. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 540
  7. 2019Graph Transformer Networkscited by 520
  8. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 499
  9. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  10. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
  11. 2023Language is All a Graph Needscited by 219
  12. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 209
  13. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  14. 2023Talk like a Graph: Encoding Graphs for Large Language Modelscited by 175
  15. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
  16. 2022NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphscited by 137
  17. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 132
  18. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  19. 2018Attention-based Graph Neural Network for Semi-supervised Learningcited by 100
  20. 2023SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learningcited by 73
  21. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  22. 2021Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced trainingcited by 54
  23. 2021Automated Self-Supervised Learning for Graphscited by 50
  24. 2020L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networkscited by 48
  25. 2022Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalizationcited by 35

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 39; 2025: 66; 2026: 72.

177 papers.

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  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  2. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  3. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  4. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  5. 2024A Survey of Large Language Models for Graphscited by 71
  6. 2024UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphscited by 60
  7. 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddingscited by 60
  8. 2024GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphscited by 58
  9. 2024GOFA: A Generative One-For-All Model for Joint Graph Language Modelingcited by 56
  10. 2024AnyGraph: Graph Foundation Model in the Wildcited by 51
  11. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  12. 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Modelscited by 36
38 more
  1. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  2. 2025RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometrycited by 32
  3. 2024How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehensioncited by 30
  4. 2024Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Treescited by 30
  5. 2024FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skippingcited by 23
  6. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  7. 2024Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learningcited by 21
  8. 2025GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Designcited by 21
  9. 2025LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Modelscited by 21
  10. 2024Distilling Large Language Models for Text-Attributed Graph Learningcited by 20
  11. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  12. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  13. 2024HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignmentcited by 17
  14. 2024UniGLM: Training One Unified Language Model for Text-Attributed Graph Embeddingcited by 14
  15. 2025Graph World Modelcited by 14
  16. 2024LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Modelcited by 13
  17. 2026Graph is a Substrate Across Data Modalitiescited by 13
  18. 2024One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphscited by 12
  19. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  20. 2025Flatten Graphs as Sequences: Transformers are Scalable Graph Generatorscited by 12
  21. 2025MLaGA: Multimodal Large Language and Graph Assistantcited by 12
  22. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigationcited by 11
  23. 2025One Prompt Fits All: Universal Graph Adaptation for Pretrained Modelscited by 11
  24. 2024Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Taskscited by 10
  25. 2026Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priorscited by 10
  26. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  27. 2024Enhancing Item Tokenization for Generative Recommendation through Self-Improvementcited by 9
  28. 2025Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learningcited by 9
  29. 2024LOGIN: A Large Language Model Consulted Graph Neural Network Training Frameworkcited by 8
  30. 2024GraphAgent: Agentic Graph Language Assistantcited by 8
  31. 2025Model Generalization on Text Attribute Graphs: Principles with Large Language Modelscited by 8
  32. 2025GiGL: Large-Scale Graph Neural Networks at Snapchatcited by 8
  33. 2025LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graphcited by 8
  34. 2024LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modelingcited by 7
  35. 2024Multi-View Empowered Structural Graph Wordification for Language Modelscited by 7
  36. 2025Towards A Universal Graph Structural Encodercited by 7
  37. 2025Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Datacited by 7
  38. 2025GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoningcited by 7

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