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
- 2. Trung Dung Hoang
- 3. Hongzhi Yin
- 4. Matthias Weidlich
- 5. Quoc Viet Hung Nguyen
- 6. Karl Aberer
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0019005923 |
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