# Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

arXiv: https://arxiv.org/abs/2405.13934 · HTML: https://mlsanity.com/paper/2405.13934/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2024.

## Built on by (most cited first, 30 of 42)

- [SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptation](https://mlsanity.com/paper/2502.05424/) (2025)
- [Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees](https://mlsanity.com/paper/2412.16441/) (2024)
- [Can LLMs Convert Graphs to Text-Attributed Graphs?](https://mlsanity.com/paper/2412.10136/) (2024)
- [GraphFM: A generalist graph transformer that learns transferable representations across diverse domains](https://mlsanity.com/paper/2407.11907/) (2024)
- [Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement](https://mlsanity.com/paper/2505.12684/) (2025)
- [GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning](https://mlsanity.com/paper/2511.05592/) (2025)
- [DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach](https://mlsanity.com/paper/2501.15142/) (2025)
- [One Prompt Fits All: Universal Graph Adaptation for Pretrained Models](https://mlsanity.com/paper/2509.22416/) (2025)
- [GCoT: Chain-of-Thought Prompt Learning for Graphs](https://mlsanity.com/paper/2502.08092/) (2025)
- [Towards Graph Foundation Models: A Transferability Perspective](https://mlsanity.com/paper/2503.09363/) (2025)
- [GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs](https://mlsanity.com/paper/2505.11125/) (2025)
- [Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs](https://mlsanity.com/paper/2412.17609/) (2024)
- [SA2GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentation](https://mlsanity.com/paper/2512.07857/) (2025)
- [MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Training](https://mlsanity.com/paper/2602.22645/) (2026)
- [Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models](https://mlsanity.com/paper/2603.00618/) (2026)
- [RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.15124/) (2026)
- [Reliable and Compact Graph Fine-tuning via GraphSparse Prompting](https://mlsanity.com/paper/2410.21749/) (2024)
- [Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Models](https://mlsanity.com/paper/2511.17982/) (2025)
- [LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training](https://mlsanity.com/paper/2602.22660/) (2026)
- [Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights](https://mlsanity.com/paper/2603.10033/) (2026)
- [Bridging Input Feature Spaces Towards Graph Foundation Models](https://mlsanity.com/paper/2605.04834/) (2026)
- [The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning](https://mlsanity.com/paper/2606.06397/) (2026)
- [A Survey of Cross-domain Graph Learning: Progress and Future Directions](https://mlsanity.com/paper/2503.11086/) (2025)
- [Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks](https://mlsanity.com/paper/2511.17989/) (2025)
- [Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding](https://mlsanity.com/paper/2602.13075/) (2026)
- [Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG](https://mlsanity.com/paper/2603.07179/) (2026)
- [Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts](https://mlsanity.com/paper/2608.06031/) (2026)
- [Relation-Aware Graph Foundation Model](https://mlsanity.com/paper/2505.12027/) (2025)
- [Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligence](https://mlsanity.com/paper/2603.21601/) (2026)
- [Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment](https://mlsanity.com/paper/2605.00731/) (2026)

## Abstract (arXiv)

Given the ubiquity of graph data, it is intriguing to ask: Is it possible to train a graph foundation model on a broad range of graph data across diverse domains? A major hurdle toward this goal lies in the fact that graphs from different domains often exhibit profoundly divergent characteristics. Although there have been some initial efforts in integrating multi-domain graphs for pre-training, they primarily rely on textual descriptions to align the graphs, limiting their application to text-attributed graphs. Moreover, different source domains may conflict or interfere with each other, and their relevance to the target domain can vary significantly. To address these issues, we propose MDGPT, a text free Multi-Domain Graph Pre-Training and adaptation framework designed to exploit multi-domain knowledge for graph learning. First, we propose a set of domain tokens to to align features across source domains for synergistic pre-training. Second, we propose a dual prompts, consisting of a unifying prompt and a mixing prompt, to further adapt the target domain with unified multi-domain knowledge and a tailored mixture of domain-specific knowledge. Finally, we conduct extensive experiments involving six public datasets to evaluate and analyze MDGPT, which outperforms prior art by up to 37.9%.