SCGFM-ART: Amortized Relational Transport for Structure-Centric Graph Foundation Models

arXiv:2609.20419 · cs.LG, cs.AI · Submitted 2026-09-17 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: 21 pages, 6 figures

Code: https://github.com/Xd-He/SCGFM-ART

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 35× in this paper · explained in 1 episode
• Foundation Model — 24× in this paper · explained in 3 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Foundation Models — 15× in this paper · explained in 7 episodes
• Embeddings — 13× in this paper · explained in 2 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Amortized Relational Transport — 10× in this paper
• Heterogeneous Graph — 10× in this paper · explained in 2 episodes
• Amortization — 7× in this paper · explained in 1 episode
• International Conference on Machine Learning — 6× in this paper
• Optimal Transport — 5× in this paper · explained in 5 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Wasserstein Distance — 3× in this paper · explained in 4 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• RAG — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 1× in this paper
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Personalized Page Rank — 1× in this paper
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

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