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All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis

Summary: Unified exposition of All-Distances Sketches (ADS) plus Historic Inverse Probability (HIP) estimators for scalable, near-linear per-node sketching of massive graphs. HIP halves variance of prior neighborhood-size estimates, yields polynomial gains for broader queries, is unbiased/simple, and empirically outperforms HyperLogLog. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1621
Venue
PODS
Year
2014
Pagerank
8.1191853e-05
Overall Rank
2,792 | 80.85%
DOI
10.1145/2594538.2594546

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{cohen_pods14,
        address = {New York, NY, USA},
        series = {{PODS} '14},
        title = {{All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis}},
        url = {https://dl.acm.org/doi/10.1145/2594538.2594546},
        doi = {10.1145/2594538.2594546},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Cohen, Edith},
        year = {2014}
}

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