EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data
Summary: EmbedX is a C++ industrial distributed framework that unifies scalable embedding training for both large graphs and extremely high-dimensional sparse features, supporting deep sparse models, network embedding, GNNs and joint graph–sparse learning. It uses distributed server layers and optimized parameter/graph operators to scale to billions of nodes/edges/dimensions, yields ~10× training speedups on Tencent workloads, and is open-sourced and production-deployed. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yuanhang Zou (Tencent)
- 2. Zhihao Ding (Hong Kong Polytechnic University)
- 3. Jieming Shi (Hong Kong Polytechnic University)
- 4. Shuting Guo (Tencent)
- 5. Chunchen Su (Tencent)
- 6. Yafei Zhang (Tencent)
BibTeX Citation
@article{zou_vldb23,
title = {{EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data}},
author = {Zou, Yuanhang and Ding, Zhihao and Shi, Jieming and Guo, Shuting and Su, Chunchen and Zhang, Yafei},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3543--3556},
doi = {10.14778/3611540.3611546},
url = {https://doi.org/10.14778/3611540.3611546},
year = {2023}
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 223 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00024182473 |
| 2,485 | HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | 2022 | VLDB | 8.5145736e-05 |
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