Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation
Summary: Lighter-X decouples propagation and compresses adjacency and embeddings in graph recommenders, reducing parameters from O(nd) to O(hd), h≪n. Plug-and-play integration preserves performance, with better results using just 1% of LightGCN’s parameters on large graphs. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yanping Zheng (Renmin University of China)
- 2. Zhewei Wei (Renmin University of China)
- 3. Frank de Hoog (Commonwealth Scientific and Industrial Research Organisation; Data 61)
- 4. Xu Chen (Renmin University of China)
- 5. Hongteng Xu (Renmin University of China)
- 6. Yuhang Ye (Huawei)
- 7. Jiadeng Huang (Huawei)
BibTeX Citation
@article{zheng_vldb25,
title = {{Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation}},
author = {Zheng, Yanping and Wei, Zhewei and de Hoog, Frank and Chen, Xu and Xu, Hongteng and Ye, Yuhang and Huang, Jiadeng},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {3721--3729},
doi = {10.14778/3749646.3749649},
url = {https://doi.org/10.14778/3749646.3749649},
year = {2025}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,682 | Accelerating Recommendation System Training by Leveraging Popular Choices | 2022 | VLDB | 8.1423493e-05 |
| 5,727 | Efficient Tree-SVD for Subset Node Embedding over Large Dynamic Graphs | 2023 | SIGMOD | 6.0167127e-05 |
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