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
- 2. Zhihao Ding
- 3. Jieming Shi
- 4. Shuting Guo
- 5. Chunchen Su
- 6. Yafei Zhang
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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 |
|---|---|---|---|---|
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 2,677 | HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | 2022 | VLDB | 8.3268401e-05 |