Graph Foundation Models: State of the Art and Future Directions
Summary: Tutorial taxonomy of Graph Foundation Models, distinguishing LLM-integrated and GNN-native paradigms across five implementation families. Highlights transfer across graphs, tasks, and modalities, plus data-management challenges in scalable graph construction and cross-domain integration. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Alexander Zhou (Hong Kong Polytechnic University)
- 2. Haoyang Li (Hong Kong Polytechnic University)
- 3. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{zhou_vldb26,
title = {{Graph Foundation Models: State of the Art and Future Directions}},
author = {Zhou, Alexander and Li, Haoyang and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4918--4921},
doi = {10.14778/3827998.3828150},
url = {https://doi.org/10.14778/3827998.3828150},
year = {2026}
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|---|---|---|---|---|
| 6,669 | Efficient Training of Graph Neural Networks on Large Graphs | 2024 | VLDB | 5.7147311e-05 |
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