{"total":40,"rows":[{"id":"2508.20906","title":"Turning Tabular Foundation Models into Graph Foundation Models","created":"2025-08-28","cited_by_count":35,"area":null},{"id":"2509.21489","title":"GraphPFN: A Prior-Data Fitted Graph Foundation Model","created":"2025-09-25","cited_by_count":21,"area":null},{"id":"2505.12684","title":"Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement","created":"2025-05-19","cited_by_count":14,"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":"2509.22416","title":"One Prompt Fits All: Universal Graph Adaptation for Pretrained Models","created":"2025-09-26","cited_by_count":11,"area":null},{"id":"2505.11125","title":"GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs","created":"2025-05-16","cited_by_count":7,"area":null},{"id":"2510.17457","title":"Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models","created":"2025-10-20","cited_by_count":7,"area":null},{"id":"2510.21131","title":"Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications","created":"2025-10-24","cited_by_count":6,"area":null},{"id":"2512.07857","title":"SA$^{2}$GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation","created":"2025-11-26","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":"2605.08689","title":"Structure-Centric Graph Foundation Model via Geometric Bases","created":"2026-05-09","cited_by_count":5,"area":null},{"id":"2606.24509","title":"A Fair Evaluation of Graph Foundation Models for Node Property Prediction","created":"2026-06-23","cited_by_count":4,"area":null},{"id":"2602.07573","title":"Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure","created":"2026-02-07","cited_by_count":3,"area":null},{"id":"2603.10033","title":"Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights","created":"2026-02-28","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":"2606.06397","title":"The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning","created":"2026-06-04","cited_by_count":3,"area":null},{"id":"2503.11086","title":"A Survey of Cross-domain Graph Learning: Progress and Future Directions","created":"2025-03-14","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":"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":"2508.04594","title":"GraphProp: Training the Graph Foundation Models using Graph Properties","created":"2025-08-06","cited_by_count":1,"area":null},{"id":"2603.21601","title":"Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence","created":"2026-03-23","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":"2605.14809","title":"GFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2605.25771","title":"MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training","created":"2026-05-25","cited_by_count":1,"area":null},{"id":"2606.22462","title":"Adaptive Recurrent Message Passing for Test Time Computing on Graphs","created":"2026-06-21","cited_by_count":1,"area":null},{"id":"2607.28980","title":"Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models","created":"2026-07-31","cited_by_count":1,"area":null},{"id":"2609.20419","title":"SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models","created":"2026-09-17","cited_by_count":1,"area":null},{"id":"2509.08401","title":"Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models","created":"2025-09-10","cited_by_count":0,"area":null},{"id":"2512.00716","title":"Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift","created":"2025-11-30","cited_by_count":0,"area":null},{"id":"2601.21067","title":"Out-of-Distribution Generalization in Graph Foundation Models","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2602.04244","title":"GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning","created":"2026-02-04","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":"2606.03270","title":"Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2607.27966","title":"What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2609.06668","title":"Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach","created":"2026-09-06","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":"2609.30894","title":"Training Graph Foundation Models on The Web Graph","created":"2026-09-25","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},{"id":"2610.14748","title":"More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}