Revisiting the Design of In-Memory Dynamic Graph Storage
Summary: Proposes a common abstraction and test framework to compare in-memory dynamic graph storage (LLAMA, Aspen, LiveGraph, Teseo, Sortledton) for read/write, space, and concurrency. Shows large memory overhead (Aspen 3.3–10.8x CSR; fine-grained 4.1–8.9x), architecture-aware bottlenecks, and high-degree vertex contention, suggesting future directions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jixian Su
- 2. Chiyu Hao
- 3. Shixuan Sun
- 4. Hao Zhang
- 5. Sen Gao
- 6. Jiaxin Jiang
- 7. Yao Chen
- 8. Chenyi Zhang
- 9. Bingsheng He
- 10. Minyi Guo
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,082 | Gem: Scalable Monotonic Graph Processing Beyond Billion-Scale on a Single Machine | 2026 | SIGMOD | 4.1945683e-05 |
| 10,161 | Enabling Efficient Direct Update on Rule-Based Compressed Graph | 2026 | SIGMOD | 4.1945683e-05 |
| 10,200 | RadixGraph: A Fast, Space-Optimized Data Structure for Dynamic Graph Storage | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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