Scalable Robust Graph Embedding with Spark
Summary: Scales graph embedding by partitioning graphs into subgraphs, learning local embeddings, and reconciling them. Distributed decomposition in Spark preserves embedding quality, reduces communication, and enables fault tolerance for large graphs. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Chi Thang Duong (EPFL)
- 2. Trung Dung Hoang (EPFL)
- 3. Hongzhi Yin (University of Queensland)
- 4. Matthias Weidlich (Humboldt-Universität zu Berlin)
- 5. Quoc Viet Hung Nguyen (Griffith University)
- 6. Karl Aberer (EPFL)
BibTeX Citation
@article{duong_vldb22,
title = {{Scalable Robust Graph Embedding with Spark}},
author = {Duong, Chi Thang and Hoang, Trung Dung and Yin, Hongzhi and Weidlich, Matthias and Nguyen, Quoc Viet Hung and Aberer, Karl},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {4},
pages = {914--922},
doi = {10.14778/3503585.3503599},
url = {https://doi.org/10.14778/3503585.3503599},
year = {2022}
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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 |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
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