{"total":31,"rows":[{"id":"2311.16534","title":"Graph Prompt Learning: A Comprehensive Survey and Beyond","created":"2023-11-28","cited_by_count":61,"area":null},{"id":"2503.00750","title":"Edge Prompt Tuning for Graph Neural Networks","created":"2025-03-02","cited_by_count":18,"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":"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":"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":"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":"2408.05920","title":"Urban Region Pre-training and Prompting: A Graph-based Approach","created":"2024-08-12","cited_by_count":8,"area":null},{"id":"2401.03597","title":"Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs","created":"2024-01-07","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":"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":"2507.07405","title":"HGMP:Heterogeneous Graph Multi-Task Prompt Learning","created":"2025-07-10","cited_by_count":4,"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":"2406.10498","title":"A Unified Graph Selective Prompt Learning for Graph Neural Networks","created":"2024-06-15","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":"2602.13075","title":"Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding","created":"2026-02-13","cited_by_count":2,"area":null},{"id":"2408.14520","title":"Towards Graph Prompt Learning: A Survey and Beyond","created":"2024-08-26","cited_by_count":1,"area":null},{"id":"2410.10089","title":"PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2507.09132","title":"Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning","created":"2025-07-12","cited_by_count":1,"area":null},{"id":"2509.21783","title":"Prompt-guided Disentangled Representation for Action Recognition","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2601.02662","title":"When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning","created":"2026-01-06","cited_by_count":1,"area":null},{"id":"2605.00731","title":"Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment","created":"2026-05-01","cited_by_count":1,"area":null},{"id":"2605.15888","title":"CHoE: Cross-Domain Heterogeneous Graph Prompt Learning via Structure-Conditioned Experts","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2502.16281","title":"FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths","created":"2025-02-22","cited_by_count":0,"area":null},{"id":"2510.22555","title":"Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers","created":"2025-10-26","cited_by_count":0,"area":null},{"id":"2601.00229","title":"Robust Graph Fine-Tuning with Adversarial Graph Prompting","created":"2026-01-01","cited_by_count":0,"area":null},{"id":"2603.27685","title":"CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning","created":"2026-03-29","cited_by_count":0,"area":null},{"id":"2610.15427","title":"Riemannian Graph Tokenizer for Structural Knowledge Transfer","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}