Improving Graph Compression for Efficient Resource-Constrained Graph Analytics
Summary: Laconic: a rule-based parallel graph compressor that minimizes memory overhead during compression and produces much smaller graphs to reduce peak memory for analytics. Versus prior work it cuts peak memory (~70%/~66%), speeds rule compression (~93%), and achieves 2.47× compression ratio and 2.12× runtime improvements. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Qian Xu
- 2. Juan Yang
- 3. Feng Zhang
- 4. Zheng Chen
- 5. Jiawei Guan
- 6. Kang Chen
- 7. Ju Fan
- 8. Youren Shen
- 9. Ke Yang
- 10. Yu Zhang
- 11. Xiaoyong Du
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,606 | Tribase: A Vector Data Query Engine for Reliable and Lossless Pruning Compression using Triangle Inequalities | 2025 | SIGMOD | 4.6967106e-05 |
| 9,879 | HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search | 2026 | SIGMOD | 4.2643674e-05 |
| 10,161 | Enabling Efficient Direct Update on Rule-Based Compressed Graph | 2026 | SIGMOD | 4.1945683e-05 |
| 10,192 | Performant Synchronization in Geo-Distributed Databases | 2026 | SIGMOD | 4.1945683e-05 |
| 10,981 | Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality | 2024 | SIGMOD | 4.1945683e-05 |
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