Efficient Personalized PageRank Computation: The Power of Variance-Reduced Monte Carlo Approaches
Summary: Two variance-reduction techniques for Personalized PageRank: power-iteration variance reduction and progressive sampling. Integrates these with random-walk and spanning-forest Monte Carlo PPR to achieve higher accuracy at equal or lower runtime than state-of-the-art bidirectional algorithms, leveraging historical sampling. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Meihao Liao
- 2. Rong-Hua Li
- 3. Qiangqiang Dai
- 4. Hongyang Chen
- 5. Hongchao Qin
- 6. Guoren Wang
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,028 | One Index for All: Towards Efficient Personalized PageRank Computation for Every Damping Factor | 2026 | SIGMOD | 4.1945683e-05 |
| 10,957 | Efficient and Provable Effective Resistance Computation on Large Graphs: an Index-based Approach | 2024 | SIGMOD | 4.1945683e-05 |
| 11,027 | BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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