Graph Foundation Models

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Graph Foundation Models study how to build reusable models that learn from graphs and transfer to new datasets and tasks, such as node classification, link prediction, clustering, and graph generation. Common approaches pretrain GNNs or graph transformers on real or synthetic graphs, encode structure with tokens or geometric representations, and adapt with prompts, in-context learning, or fine-tuning; some also align graphs with language or tabular models. Work increasingly targets transfer across different feature spaces, graph types, and domains, including knowledge graphs, molecules, and dynamic networks. Results are promising, but fair benchmarks show that carefully tuned task-specific GNNs often remain competitive or stronger, while transfer reliability, inference cost, and robustness are unresolved concerns.

Latest papers

97 papers11 papers in the last four weeks, down 8% on the four weeks before. 0.1% of all new papers.

KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Nov 6, 2025Yuanning Cui, Zequn Sun, Wei Hu +1Multi-Hop QA LLM Grounding
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing 210093, China +2
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
Sep 29, 2025Yunhao Liang, Pujun Zhang, Yuan Qu +3Graph SSL Combinatorial Optimization
Faculty of Engineering, The University of Hong Kong, Hong Kong SAR, China · Costello College of Business, George Mason University, VA, USA · Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China +1
GraphPFN: A Prior-Data Fitted Graph Foundation Model
Sep 25, 2025Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov +2Synthetic Data Pretraining In-Context Learning
1HSE University · 2Yandex Research
Turning Tabular Foundation Models into Graph Foundation Models
Aug 28, 2025Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov +2Graph Foundation Models Node Classification
HSE University · Yandex Research
Relation-Aware Graph Foundation Model
May 17, 2025Jianxiang Yu, Jiapeng Zhu, Yibo Zhao +4Graph Neural Networks Graph Representation Learning
School of Data Science and Engineering, East China Normal University · Ant Group
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Mar 5, 2025Xi Zhu, Haochen Xue, Ziwei Zhao +7Text-Attributed Graph Learning Graph Foundation Models
1Rutgers University · 2Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · University of Science and Technology of China +2
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Feb 13, 2025Hezhe Qiao, Chaoxi Niu, Ling Chen +1Graph-Based Anomaly Detection Anomaly Detection
School of Computing and Information Systems Singapore Management University Singapore · Australian Artificial Intelligence Institute University of Technology Sydney Sydney, Australia

Nov 6, 2025J/K move · Enter open · S save

KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering

Yuanning Cui, Zequn Sun, Wei Hu, Zhangjie Fu

School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing 210093, China+2

Multi-Hop QA LLM Grounding LLM Reasoning with Graphs Graph-Based Retrieval Graph Foundation Models Knowledge Graph Question Answering

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Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. However, existing methods that rely on finetuned LLMs or GNN retrievers are limited by dataset-specific tuning and scalability on large or unseen graphs. We propose the LLM-KGFR collaborative framework, where an LLM works with a structured retriever, the Knowledge Graph Foundation Retriever (KGFR). KGFR encodes relations using LLM-generated descriptions and initializes entities based on their roles in the question, enabling zero-shot generalization to unseen KGs. To handle large graphs efficiently, it employs Asymmetric Progressive Propagation (APP)- a stepwise expansion that selectively limits high-degree nodes while retaining informative paths. Through node-, edge-, and path-level interfaces, the LLM iteratively requests candidate answers, supporting facts, and reasoning paths, forming a controllable reasoning loop. Experiments demonstrate that LLM-KGFR achieves strong performance while maintaining scalability and generalization, providing a practical solution for KG-augmented reasoning.