Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph
Summary: Introduce Temporal-based Bipartite Graph and temporal preferential-attachment similarity (TPASim) to encode concurrent node activity for temporal graph embeddings. LTGE factorizes a 2m-sparse temporal matrix provably equivalent to dense TPASim (reducing complexity by n^2/m) and LTGEInc uses an incremental SVD with guarantees to update embeddings, scaling to 17M nodes/1.3B edges and outperforming prior temporal methods. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yifan Song (Hong Kong University of Science and Technology)
- 2. Xiaolong Chen (Hong Kong University of Science and Technology)
- 3. Wenqing Lin (Tencent)
- 4. Jia Li (Hong Kong University of Science and Technology)
- 5. Chen Zhang (Zhejiang CreateLink Technology)
- 6. Yan Zhou (Zhejiang CreateLink Technology)
- 7. Lei Chen (Hong Kong University of Science and Technology)
- 8. Jing Tang (Hong Kong University of Science and Technology)
BibTeX Citation
@article{song_vldb25,
title = {{Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph}},
author = {Song, Yifan and Chen, Xiaolong and Lin, Wenqing and Li, Jia and Zhang, Chen and Zhou, Yan and Chen, Lei and Tang, Jing},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {4},
pages = {929--942},
doi = {10.14778/3717755.3717756},
url = {https://doi.org/10.14778/3717755.3717756},
year = {2025}
}
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| 10,382 | LLM-Powered Interactive Graph Search: A Scalable and Practical Approach | 2026 | SIGMOD | 5.093636e-05 |
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