Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs
Summary: Memory-aware framework for second-order random walks on billion-edge graphs, addressing memory blowups of conventional sampling with a cost model for node sampling. Acceptance-rejection sampling with per-node cost optimization allocates under a memory budget while minimizing time, delivering ~90% memory reduction and practical APIs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yingxia Shao
- 2. Shiyue Huang
- 3. Xupeng Miao
- 4. Bin Cui
- 5. Lei Chen
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,058 | ThunderRW: An In-Memory Graph Random Walk Engine | 2021 | VLDB | 5.2310254e-05 |
| 7,212 | Space-Efficient Random Walks on Streaming Graphs | 2023 | VLDB | 4.7989929e-05 |
| 7,363 | An I/O-Efficient Disk-based Graph System for Scalable Second-Order Random Walk of Large Graphs | 2022 | VLDB | 4.7523184e-05 |
| 11,017 | FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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