{"total":167,"rows":[{"id":"2310.00149","title":"One for All: Towards Training One Graph Model for All Classification Tasks","created":"2023-09-29","cited_by_count":243,"area":null},{"id":"2310.13023","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","created":"2023-10-19","cited_by_count":226,"area":null},{"id":"2310.11829","title":"Graph Foundation Models: Concepts, Opportunities and Challenges","created":"2023-10-18","cited_by_count":103,"area":null},{"id":"2402.09834","title":"All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining","created":"2024-02-15","cited_by_count":86,"area":null},{"id":"2402.11235","title":"ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs","created":"2024-02-17","cited_by_count":85,"area":null},{"id":"2411.06070","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","created":"2024-11-09","cited_by_count":85,"area":null},{"id":"2403.01121","title":"OpenGraph: Towards Open Graph Foundation Models","created":"2024-03-02","cited_by_count":78,"area":null},{"id":"2402.13630","title":"UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs","created":"2024-02-21","cited_by_count":66,"area":null},{"id":"2402.07197","title":"GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks","created":"2024-02-11","cited_by_count":63,"area":null},{"id":"2502.05424","title":"SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation","created":"2025-02-08","cited_by_count":62,"area":null},{"id":"2311.16534","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","created":"2023-11-28","cited_by_count":61,"area":null},{"id":"2408.14512","title":"LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings","created":"2024-08-25","cited_by_count":61,"area":null},{"id":"2410.10329","title":"GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs","created":"2024-10-14","cited_by_count":58,"area":null},{"id":"2407.09709","title":"GOFA: A Generative One-For-All Model for Joint Graph Language Modeling","created":"2024-07-12","cited_by_count":57,"area":null},{"id":"2408.10700","title":"AnyGraph: Graph Foundation Model in the Wild","created":"2024-08-20","cited_by_count":53,"area":"moe"},{"id":"2306.06063","title":"Virtual Node Tuning for Few-shot Node Classification","created":"2023-06-09","cited_by_count":46,"area":null},{"id":"2406.10727","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","created":"2024-06-15","cited_by_count":44,"area":null},{"id":"2311.15317","title":"Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs","created":"2023-11-26","cited_by_count":39,"area":null},{"id":"2302.12449","title":"SGL-PT: A Strong Graph Learner with Graph Prompt Tuning","created":"2023-02-24","cited_by_count":35,"area":null},{"id":"2310.07365","title":"GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning","created":"2023-10-11","cited_by_count":33,"area":null},{"id":"2310.15318","title":"HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks","created":"2023-10-23","cited_by_count":33,"area":null},{"id":"2404.14928","title":"Graph Machine Learning in the Era of Large Language Models (LLMs)","created":"2024-04-23","cited_by_count":33,"area":null},{"id":"2412.16441","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","created":"2024-12-21","cited_by_count":32,"area":null},{"id":"2408.12594","title":"Non-Homophilic Graph Pre-Training and Prompt Learning","created":"2024-08-22","cited_by_count":31,"area":null},{"id":"2406.05346","title":"ProG: A Graph Prompt Learning Benchmark","created":"2024-06-08","cited_by_count":29,"area":null},{"id":"2410.14886","title":"Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts","created":"2024-10-18","cited_by_count":28,"area":null},{"id":"2410.12288","title":"A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning","created":"2024-10-16","cited_by_count":25,"area":null},{"id":"2502.09254","title":"AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection","created":"2025-02-13","cited_by_count":23,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","cited_by_count":22,"area":null},{"id":"2311.14064","title":"HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding","created":"2023-11-23","cited_by_count":21,"area":"mllm"},{"id":"2409.16670","title":"GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning","created":"2024-09-25","cited_by_count":21,"area":null},{"id":"2402.05962","title":"EXGC: Bridging Efficiency and Explainability in Graph Condensation","created":"2024-02-05","cited_by_count":20,"area":null},{"id":"2503.00750","title":"Edge Prompt Tuning for Graph Neural Networks","created":"2025-03-02","cited_by_count":18,"area":null},{"id":"2311.16716","title":"GraphPro: Graph Pre-training and Prompt Learning for Recommendation","created":"2023-11-28","cited_by_count":17,"area":null},{"id":"2404.14809","title":"A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications","created":"2024-04-23","cited_by_count":17,"area":null},{"id":"2402.05952","title":"Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques","created":"2024-02-04","cited_by_count":16,"area":null},{"id":"2308.13821","title":"A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions","created":"2023-08-26","cited_by_count":15,"area":null},{"id":"2405.17984","title":"Cross-Context Backdoor Attacks against Graph Prompt Learning","created":"2024-05-28","cited_by_count":14,"area":"safety"},{"id":"2411.06427","title":"UniGAD: Unifying Multi-level Graph Anomaly Detection","created":"2024-11-10","cited_by_count":14,"area":null},{"id":"2308.06960","title":"Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks","created":"2023-08-14","cited_by_count":13,"area":null},{"id":"2401.09769","title":"A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions","created":"2024-01-18","cited_by_count":13,"area":null},{"id":"2402.13556","title":"Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective","created":"2024-02-21","cited_by_count":13,"area":null},{"id":"2412.08174","title":"Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?","created":"2024-12-11","cited_by_count":13,"area":null},{"id":"2511.05592","title":"GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning","created":"2025-11-05","cited_by_count":13,"area":null},{"id":"2412.00315","title":"One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs","created":"2024-11-30","cited_by_count":12,"area":null},{"id":"2405.13937","title":"Node-Time Conditional Prompt Learning In Dynamic Graphs","created":"2024-05-22","cited_by_count":11,"area":null},{"id":"2501.15142","title":"DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach","created":"2025-01-25","cited_by_count":11,"area":null},{"id":"2509.22416","title":"One Prompt Fits All: Universal Graph Adaptation for Pretrained Models","created":"2025-09-26","cited_by_count":11,"area":null},{"id":"2402.12161","title":"Endowing Pre-trained Graph Models with Provable Fairness","created":"2024-02-19","cited_by_count":10,"area":null},{"id":"2405.12452","title":"Prompt-Based Spatio-Temporal Graph Transfer Learning","created":"2024-05-21","cited_by_count":10,"area":null},{"id":"2410.01635","title":"Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis","created":"2024-10-02","cited_by_count":10,"area":null},{"id":"2507.11997","title":"Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection","created":"2025-07-16","cited_by_count":10,"area":null},{"id":"2312.03740","title":"A Survey on Prompting Techniques in LLMs","created":"2023-11-28","cited_by_count":9,"area":null},{"id":"2402.10380","title":"Subgraph-level Universal Prompt Tuning","created":"2024-02-16","cited_by_count":9,"area":null},{"id":"2403.16137","title":"A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective","created":"2024-03-24","cited_by_count":9,"area":null},{"id":"2406.14683","title":"TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models","created":"2024-06-20","cited_by_count":9,"area":null},{"id":"2408.03195","title":"RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning","created":"2024-08-06","cited_by_count":9,"area":"deeprl"},{"id":"2502.08092","title":"GCoT: Chain-of-Thought Prompt Learning for Graphs","created":"2025-02-12","cited_by_count":9,"area":"lr"},{"id":"2310.17394","title":"PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks","created":"2023-10-26","cited_by_count":8,"area":null},{"id":"2406.01899","title":"Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models","created":"2024-06-04","cited_by_count":8,"area":"diffusion"},{"id":"2408.05920","title":"Urban Region Pre-training and Prompting: A Graph-based Approach","created":"2024-08-12","cited_by_count":8,"area":null},{"id":"2406.08229","title":"GPT4Rec: Graph Prompt Tuning for Streaming Recommendation","created":"2024-06-12","cited_by_count":7,"area":null},{"id":"2510.16885","title":"UniGTE: Unified Graph-Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains","created":"2025-10-19","cited_by_count":7,"area":null},{"id":"2307.02813","title":"CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks","created":"2023-07-06","cited_by_count":6,"area":null},{"id":"2311.02798","title":"Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation","created":"2023-11-05","cited_by_count":6,"area":null},{"id":"2402.01440","title":"A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning","created":"2024-02-02","cited_by_count":6,"area":null},{"id":"2410.12609","title":"Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs","created":"2024-10-16","cited_by_count":6,"area":null},{"id":"2411.17676","title":"Instance-Aware Graph Prompt Learning","created":"2024-11-26","cited_by_count":6,"area":null},{"id":"2412.12155","title":"SGPT: Few-Shot Prompt Tuning for Signed Graphs","created":"2024-12-11","cited_by_count":6,"area":null},{"id":"2505.02027","title":"GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning","created":"2025-05-04","cited_by_count":6,"area":null},{"id":"2404.16339","title":"Training-Free Unsupervised Prompt for Vision-Language Models","created":"2024-04-25","cited_by_count":5,"area":"mllm"},{"id":"2410.08629","title":"Towards Cross-domain Few-shot Graph Anomaly Detection","created":"2024-10-11","cited_by_count":5,"area":null},{"id":"2508.00664","title":"DP-DGAD: A Generalist Dynamic Graph Anomaly Detector with Dynamic Prototypes","created":"2025-08-01","cited_by_count":5,"area":null},{"id":"2510.22451","title":"GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks","created":"2025-10-25","cited_by_count":5,"area":null},{"id":"2603.00618","title":"Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models","created":"2026-02-28","cited_by_count":5,"area":null},{"id":"2410.16845","title":"Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification","created":"2024-10-22","cited_by_count":4,"area":null},{"id":"2507.07405","title":"HGMP:Heterogeneous Graph Multi-Task Prompt Learning","created":"2025-07-10","cited_by_count":4,"area":null},{"id":"2601.15124","title":"RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation","created":"2026-01-21","cited_by_count":4,"area":"rag"},{"id":"2402.15680","title":"Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks","created":"2024-02-24","cited_by_count":3,"area":null},{"id":"2402.19405","title":"Navigating Hallucinations for Reasoning of Unintentional Activities","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2410.21749","title":"Reliable and Compact Graph Fine-tuning via GraphSparse Prompting","created":"2024-10-29","cited_by_count":3,"area":null},{"id":"2506.08326","title":"Graph Prompting for Graph Learning Models: Recent Advances and Future Directions","created":"2025-06-10","cited_by_count":3,"area":null},{"id":"2506.22510","title":"Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning","created":"2025-06-26","cited_by_count":3,"area":null},{"id":"2510.01248","title":"SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs","created":"2025-09-24","cited_by_count":3,"area":null},{"id":"2510.19384","title":"Learning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessment","created":"2025-10-22","cited_by_count":3,"area":null},{"id":"2512.08763","title":"Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning","created":"2025-12-09","cited_by_count":3,"area":"deeprl"},{"id":"2602.22660","title":"LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training","created":"2026-02-26","cited_by_count":3,"area":null},{"id":"2603.13434","title":"Modality-free Graph In-context Alignment","created":"2026-03-13","cited_by_count":3,"area":null},{"id":"2605.09993","title":"Learning Graph Foundation Models on Riemannian Graph-of-Graphs","created":"2026-05-11","cited_by_count":3,"area":null},{"id":"2402.10645","title":"Can Separators Improve Chain-of-Thought Prompting?","created":"2024-02-16","cited_by_count":2,"area":"lr"},{"id":"2404.01693","title":"HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning","created":"2024-04-02","cited_by_count":2,"area":null},{"id":"2406.10498","title":"A Unified Graph Selective Prompt Learning for Graph Neural Networks","created":"2024-06-15","cited_by_count":2,"area":null},{"id":"2407.13068","title":"Krait: A Backdoor Attack Against Graph Prompt Tuning","created":"2024-07-18","cited_by_count":2,"area":"safety"},{"id":"2409.00670","title":"Towards Faster Graph Partitioning via Pre-training and Inductive Inference","created":"2024-09-01","cited_by_count":2,"area":null},{"id":"2410.13974","title":"Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2411.01155","title":"HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters","created":"2024-11-02","cited_by_count":2,"area":null},{"id":"2506.14098","title":"Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks","created":"2025-06-17","cited_by_count":2,"area":null},{"id":"2507.09797","title":"A Scalable and Efficient Signal Integration System for Job Matching","created":"2025-07-13","cited_by_count":2,"area":null},{"id":"2510.04567","title":"GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning","created":"2025-10-06","cited_by_count":2,"area":null},{"id":"2601.11954","title":"Data-centric Prompt Tuning for Dynamic Graphs","created":"2026-01-17","cited_by_count":2,"area":null}],"next_cursor":"100"}