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Estimating PageRank on Graph Streams

Summary: Streaming algorithms to estimate l-step random-walk distributions, mixing time and conductance using sublinear memory and few passes. Applies to PageRank: computes additive-ε approximations in sublinear space (e.g., Õ(n M^{-4/3})) and Õ(M^{4/3}) passes for mixing time M, outperforming the O(n)-space, O(M)-pass baseline when M is large. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1446
Venue
PODS
Year
2008
Pagerank
0.00010921308
Overall Rank
1,396 | 90.43%
DOI
10.1145/1376916.1376928

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{sarma_pods08,
        address = {New York, NY, USA},
        series = {{PODS} '08},
        title = {{Estimating PageRank on Graph Streams}},
        url = {https://dl.acm.org/doi/10.1145/1376916.1376928},
        doi = {10.1145/1376916.1376928},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Sarma, Atish Das and Gollapudi, Sreenivas and Panigrahy, Rina},
        year = {2008}
}

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