{"total":43,"rows":[{"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":"2408.12594","title":"Non-Homophilic Graph Pre-Training and Prompt Learning","created":"2024-08-22","cited_by_count":31,"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":"2402.06326","title":"Prompt Learning on Temporal Interaction Graphs","created":"2024-02-09","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":"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":"2503.01900","title":"LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection","created":"2025-02-28","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":"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":"2402.11472","title":"DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning","created":"2024-02-18","cited_by_count":7,"area":null},{"id":"2406.08229","title":"GPT4Rec: Graph Prompt Tuning for Streaming Recommendation","created":"2024-06-12","cited_by_count":7,"area":null},{"id":"2412.00742","title":"Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective","created":"2024-12-01","cited_by_count":7,"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":"2603.00618","title":"Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models","created":"2026-02-28","cited_by_count":5,"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":"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":"2511.17923","title":"Towards Efficient LLM-aware Heterogeneous Graph Learning","created":"2025-11-22","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},{"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":"2608.06031","title":"Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts","created":"2026-08-06","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":"2507.09132","title":"Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning","created":"2025-07-12","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":"2605.13540","title":"Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models","created":"2026-05-13","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":"2505.05684","title":"Meta-Semantics Augmented Few-Shot Relational Learning","created":"2025-05-08","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":"2510.23469","title":"Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2512.10589","title":"THeGAU: Type-Aware Heterogeneous Graph Autoencoder and Augmentation","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.11629","title":"GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks","created":"2026-02-12","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":"2604.00399","title":"A Cross-graph Tuning-free GNN Prompting Framework","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2605.12197","title":"A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2607.08996","title":"Model Agnostic Graph Prompt Learning for Crystal Property Prediction","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2609.23547","title":"Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.32143","title":"Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}