Training Graph Foundation Models on The Web Graph

arXiv:2609.30894 · cs.AI, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Foundation Model — 31× in this paper · explained in 3 episodes
• In-Context Learning — 25× in this paper · explained in 4 episodes
• Foundation Models — 20× in this paper · explained in 7 episodes
• Clustering — 15× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• GNNs — 3× in this paper · explained in 2 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Adjusted Mutual Information — 1× in this paper
• Adjusted Rand Index — 1× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes

Sources

• SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
• Billion-Scale Graph Foundation Models
• Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
• Graph Generative Pre-trained Transformer
• Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
• Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
• Turning Tabular Foundation Models into Graph Foundation Models
• GraphPFN: A Prior-Data Fitted Graph Foundation Model
• Advancing Graph Few-Shot Learning via In-Context Learning
• Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
• Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training
• UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
• UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs
• Open Graph Benchmark: Datasets for Machine Learning on Graphs
• PRODIGY: Enabling In-context Learning Over Graphs
• Why Does Graph Learning Fail to Fully Benefit from a Text Teacher?
• GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
• GraphFM: A generalist graph transformer that learns transferable representations across diverse domains
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Are Large Language Models In-Context Graph Learners?

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection