# Graph Foundation Models: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2505.15116 · HTML: https://mlsanity.com/paper/2505.15116/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2025.

## Built on by (most cited first, 30 of 44)

- [Turning Tabular Foundation Models into Graph Foundation Models](https://mlsanity.com/paper/2508.20906/) (2025)
- [Graph is a Substrate Across Data Modalities](https://mlsanity.com/paper/2601.22384/) (2026)
- [One Prompt Fits All: Universal Graph Adaptation for Pretrained Models](https://mlsanity.com/paper/2509.22416/) (2025)
- [Molecular Representations in Implicit Functional Space via Hyper-Networks](https://mlsanity.com/paper/2601.22327/) (2026)
- [Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models](https://mlsanity.com/paper/2603.00618/) (2026)
- [Structure-Centric Graph Foundation Model via Geometric Bases](https://mlsanity.com/paper/2605.08689/) (2026)
- [OpenRFM: Dissecting Relational In-Context Learning](https://mlsanity.com/paper/2606.04320/) (2026)
- [Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models](https://mlsanity.com/paper/2601.04110/) (2026)
- [TabFORGE: A Tabular Foundation Model for Generative Modelling](https://mlsanity.com/paper/2605.09424/) (2026)
- [Learning Graph Foundation Models on Riemannian Graph-of-Graphs](https://mlsanity.com/paper/2605.09993/) (2026)
- [The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning](https://mlsanity.com/paper/2606.06397/) (2026)
- [GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization](https://mlsanity.com/paper/2509.21097/) (2025)
- [Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks](https://mlsanity.com/paper/2511.17989/) (2025)
- [BHyGNN+: Unsupervised Representation Learning for Heterophilic Hypergraphs](https://mlsanity.com/paper/2602.14919/) (2026)
- [The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning](https://mlsanity.com/paper/2510.06819/) (2025)
- [OPBench: A Graph Benchmark to Combat the Opioid Crisis](https://mlsanity.com/paper/2602.14602/) (2026)
- [Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models](https://mlsanity.com/paper/2605.06154/) (2026)
- [PermuFormer: Multi-Task Pretraining for Permutation Representation in Algebraic Combinatorics](https://mlsanity.com/paper/2609.25438/) (2026)
- [A Graph Laplacian Eigenvector-based Pre-training Method for Graph Neural Networks](https://mlsanity.com/paper/2509.02803/) (2025)
- [Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing](https://mlsanity.com/paper/2511.11046/) (2025)
- [Out-of-Distribution Generalization in Graph Foundation Models](https://mlsanity.com/paper/2601.21067/) (2026)
- [A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM](https://mlsanity.com/paper/2604.06666/) (2026)
- [Generalization in LLM Problem Solving: The Case of the Shortest Path](https://mlsanity.com/paper/2604.15306/) (2026)
- [Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning](https://mlsanity.com/paper/2604.22031/) (2026)
- [On the Safety of Graph Representation Learning](https://mlsanity.com/paper/2605.06576/) (2026)
- [A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning](https://mlsanity.com/paper/2605.12197/) (2026)
- [Deep Neural Sheaf Diffusion](https://mlsanity.com/paper/2605.19021/) (2026)
- [Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification](https://mlsanity.com/paper/2605.29228/) (2026)
- [GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning](https://mlsanity.com/paper/2605.31031/) (2026)
- [Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective](https://mlsanity.com/paper/2606.03290/) (2026)

## Abstract (arXiv)

Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems. While foundation models have transformed natural language processing, vision, and multimodal learning through large-scale pretraining and generalization, extending these capabilities to graphs -- characterized by non-Euclidean structures and complex relational semantics -- poses unique challenges and opens new opportunities. To this end, Graph Foundation Models (GFMs) aim to bring scalable, general-purpose intelligence to structured data, enabling broad transfer across graph-centric tasks and domains. This survey provides a comprehensive overview of GFMs, unifying diverse efforts under a modular framework comprising three key components: backbone architectures, pretraining strategies, and adaptation mechanisms. We categorize GFMs by their generalization scope -- universal, task-specific, and domain-specific -- and review representative methods, key innovations, and theoretical insights within each category. Beyond methodology, we examine theoretical foundations including transferability and emergent capabilities, and highlight key challenges such as structural alignment, heterogeneity, scalability, and evaluation. Positioned at the intersection of graph learning and general-purpose AI, GFMs are poised to become foundational infrastructure for open-ended reasoning over structured data. This survey consolidates current progress and outlines future directions to guide research in this rapidly evolving field. Resources are available at https://github.com/Zehong-Wang/Awesome-Foundation-Models-on-Graphs.