{"total":53,"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":"2602.08431","title":"USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation","created":"2026-02-09","cited_by_count":9,"area":null},{"id":"2510.07755","title":"FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling","created":"2025-10-09","cited_by_count":8,"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":"2602.04116","title":"Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach","created":"2026-02-04","cited_by_count":7,"area":null},{"id":"2512.11561","title":"View Space: Learning Representation across Arbitrary Graphs","created":"2025-12-12","cited_by_count":5,"area":null},{"id":"2602.22645","title":"MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training","created":"2026-02-26","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":"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":"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":"2605.04834","title":"Bridging Input Feature Spaces Towards Graph Foundation Models","created":"2026-05-06","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":"2607.17272","title":"Node4All: Learning Node Representation Beyond Datasets","created":"2026-07-19","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":"2505.13989","title":"When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty","created":"2025-05-20","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":"2607.11374","title":"Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment","created":"2026-07-13","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":"2512.14710","title":"Autonomous Source Knowledge Selection in Multi-Domain Adaptation","created":"2025-12-08","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":"2601.21309","title":"Transferable Graph Condensation from the Causal Perspective","created":"2026-01-29","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":"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":"2604.22031","title":"Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning","created":"2026-04-23","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":"2606.00808","title":"Safe-Subspace Pseudo-Label Refinement for Source-Free Graph Domain Adaptation","created":"2026-05-30","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":"2606.03315","title":"SPR: Toward a Graph Foundation Model for Transferable Graph Cognition via Spectral Patterns and Relational Geometry","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2607.26023","title":"CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer","created":"2026-07-28","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":"2608.14823","title":"Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.19858","title":"Online Test-Time Adaptation for Generalizable Dynamic Graph Anomaly Detection","created":"2026-08-20","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":"2609.25722","title":"Signed Graph Pre-Training and Prompt Learning","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2609.29398","title":"ICE: Task-Aligned Clifford Latent Fields for Multimodal Graph Foundation Models","created":"2026-09-24","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":"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}