UniTG: A Unified System for Efficient and Seamless Textual Graph Learning
Summary: UniTG unifies LM fine-tuning and GNN training for text-attributed graphs in one end-to-end system. Co-designed affinity-aware parallelism, collaborative multimodal learning, and bubble-reducing scheduling cut makespan by up to 17.3× without quality loss. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Meng Zhang (Nanyang Technological University)
- 2. Zhisheng Ye (Peking University)
- 3. Qiyu Liu (Southwest University)
- 4. Jingshu Peng (Hong Kong University of Science and Technology)
- 5. Tianwei Zhang (Nanyang Technological University)
BibTeX Citation
@article{zhang_vldb26,
title = {{UniTG: A Unified System for Efficient and Seamless Textual Graph Learning}},
author = {Zhang, Meng and Ye, Zhisheng and Liu, Qiyu and Peng, Jingshu and Zhang, Tianwei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3273--3287},
doi = {10.14778/3836663.3836688},
url = {https://doi.org/10.14778/3836663.3836688},
year = {2026}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,246 | PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel | 2023 | VLDB | 8.7637336e-05 |
| 2,279 | NeutronStar: Distributed GNN Training with Hybrid Dependency Management | 2022 | SIGMOD | 8.7062637e-05 |
| 4,825 | DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning | 2024 | VLDB | 6.3923554e-05 |
| 7,522 | NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism | 2025 | VLDB | 5.5025409e-05 |
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