Large Language Model (LLM) has revolutionized lots of areas and is now making its way into conventional graph representation learning. Some empirical evidence already shows that, with the powerful LLM, graph neural network doesn't seem to be irreplaceable and this trend is inevitable and unstoppable. So we will try to embrace it and keep track of its development. In this repository, we collect papers focusing on LLM for graph learning tasks at node, edge and graph levels. If you find that we miss any related work or we need to update information (publication, code, blog, etc. ) of the following papers, feel free to let us know by email (sitao.luan@mail.mcgill.ca, luansito@mila.quebec, jiaqi.zhu@mail.mcgill.ca).
🔥Sitao's Recommendation 👑Reported results surpass SOTA GNNs' results on node classification tasks
- (KDD 2022) GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks [paper][code]
- (ICLR 2023) Learning on Large-scale Text-attributed Graphs via Variational Inference [paper][code]
- (KDD 2023) Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications [paper]
- (KDD 2023) All in One: Multi-task Prompting for Graph Neural Networks [paper][code]
- (KDD 2023) Virtual Node Tuning for Few-shot Node Classification [paper]
- (WWW 2023) GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks [paper][code][blog in Chinese]
- (SIGIR 2023) Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting [paper][code]
- (NeurIPS 2023) GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning [paper] [code]
- (NeurIPS 2023) WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding [paper]
- (NeurIPS 2023) Universal Prompt Tuning for Graph Neural Networks [paper] [Code]
- (NeurIPS 2023) Can Language Models Solve Graph Problems in Natural Language? [paper][code]
- (EMNLP 2023) GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs [paper][code]
- (EMNLP 2023) Pretraining Language Models with Text-Attributed Heterogeneous Graphs [paper][code]
- (ICLR 2024) One for All: Towards Training One Graph Model for All Classification Tasks [paper] [code]
- 🔥(ICLR 2024) Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [paper][code]
- (ICLR 2024) Label-free Node Classification on Graphs with Large Language Models (LLMS) [paper] [code]
- (WWW 2024) Can GNN be Good Adapter for LLMs? [paper]
- (WWW 2024) High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs [paper]
- (arXiv 2023.02) SGL-PT: A Strong Graph Learner with Graph Prompt Tuning [paper] [blog in Chinese]
- (openreview) Pretrained Language Models to Solve Graph Tasks in Natural Language [paper]
- (arXiv 2023.07) Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [paper] [code][blog in Chinese]
- (arXiv 2023.07) Can Large Language Models Empower Molecular Property Prediction? [paper] [code]
- (arXiv 2023.07) Prompt Tuning on Graph-augmented Low-resource Text Classification [paper] [code]
- (arXiv 2023.07) Prompt-Based Zero- and Few-Shot Node Classification: A Multimodal Approach [paper]
- 🔥👑(arXiv 2023.08) Natural Language is All a Graph Needs [paper] [code]
- (arXiv 2023.08) SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [paper] [code]
- (arXiv 2023.09) Unleashing the Power of Graph Learning through LLM-based Autonomous Agents [paper]
- 🔥(arXiv 2023.09) Can LLMs Effectively Leverage Structural Information for Graph Learning: When and Why [paper] [code]
- (arXiv 2023.09) Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs [paper]
- (arXiv 2023.09) TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning [paper]
- 🔥(arXiv 2023.10) GraphText: Graph Reasoning in Text Space [paper] [code]
- (arXiv 2023.10) GraphLLM: Boosting Graph Reasoning Ability of Large Language Model [paper][code]
- (arXiv 2023.10) Talk Like a Graph: Encoding Graphs for Large Language Models [paper]
- (arXiv 2023.10) Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data [paper]
- (arXiv 2023.10) Empower Text-Attributed Graphs Learning with Large Language Models (LLMs) [paper]
- (openreview) Efficient Large Language Models Fine-Tuning on Graphs [paper]
- (openreview) Spatio-Temporal Graph Learning with Large Language Model [paper]
- (openreview) Simple Yet Effective Spatio-Temporal Prompt Learning [paper]
- (openreview) GraphGPT: Graph Learning with Generative Pre-trained Transformers [paper]
- (openreview) GraphAgent: Exploiting Large Language Models for Interpretable Learning on Text-attributed Graphs [paper]
- (arXiv 2023.10) Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models [paper]
- 🔥👑(arXiv 2023.10) GraphGPT: Graph Instruction Tuning for Large Language Models [paper][code][project page][blog in Chinese]
- 👑(arXiv 2023.10) Graph Agent: Explicit Reasoning Agent for Graphs [paper]
- (arXiv 2023.10) LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs? [paper]
- (arXiv 2023.10) Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [paper]
- (arXiv 2023.10) HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks [paper]
- (arXiv 2023.10) Enhancing Graph Neural Networks with Structure-Based Prompt [paper]
- (arXiv 2023.10) ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt [paper]
- (arXiv 2023.10) Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder [paper][code]
- (arXiv 2023.11) Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models [paper]
- (arXiv 2023.11) Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs [paper]
- (arXiv 2023.12) Efficient Large Language Models Fine-Tuning On Graphs [paper]
- (Instruction Workshop @ NeurIPS 2023) Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning [paper]
- (arXiv 2024.01) Graph Language Models [paper][code]
- (arXiv 2024.01) Scalable Link Prediction on Large-Scale Heterogeneous Graphs with Large Language Models [paper]
- (arXiv 2024.01) How Can Large Language Models Understand Spatial-Temporal Data? [paper]
- (arXiv 2024.01) Efficient Tuning and Inference for Large Language Models on Textual Graphs [paper]
- (arXiv 2024.02) Similarity-based Neighbor Selection for Graph LLMs [paper] [code]
- (arXiv 2024.02) Large Language Model Meets Graph Neural Network in Knowledge Distillation [paper]
- (arXiv 2024.02) Let Your Graph Do the Talking: Encoding Structured Data for LLMs [paper]
- (arXiv 2024.02) GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks [paper]
- (arXiv 2024.02) InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment [paper]
- (arXiv 2024.02) HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs [paper]
- (arXiv 2024.02) Can we soft prompt LLMs for graph learning tasks? [paper]
- (arXiv 2024.02) Distilling Large Language Models for Text-Attributed Graph Learning [paper]
- (arXiv 2024.02) All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining [paper]
- (arXiv 2024.02) UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language [paper]
- (arXiv 2024.02) HiGPT: Heterogeneous Graph Language Model [paper]
- (arXiv 2024.02) GraphEdit: Large Language Models for Graph Structure Learning [paper]
- (arXiv 2024.02) LLaGA: Large Language and Graph Assistant [paper][code]
- (arXiv 2024.02) Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text [paper]
- (arXiv 2023.10) Towards Graph Foundation Models: A Survey and Beyond [paper]
- (IEEE Intelligent Systems 2023) Integrating Graphs with Large Language Models: Methods and Prospects [paper]
- (NeurIPS GLFrontiers 2023) Integrating Graphs with Large Language Models: Methods and Prospects [paper]
- (arXiv 2023.11) A Survey of Graph Meets Large Language Model: Progress and Future Directions [paper]
- (arXiv 2023.12) Large Language Models on Graphs: A Comprehensive Survey [paper]
- (arXiv 2024.02) Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques [paper]
- (arXiv 2024.02) Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models [paper]