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Fast Incremental and Personalized PageRank
Summary: Monte Carlo maintenance of global PageRank under adversarial edge updates in random order achieves O(n log m/ε²) total work, with deletions equally efficient. For power-law personalized PageRank, precomputed walks enable fast top-k queries and real-time distributed-network performance, validated on Twitter.
(summarized by gpt-5.6-luna on Jul 24 2026)
Paper ID
h82439787bf0b4b1e
Venue
VLDB
Year
2011
Pagerank
0.00016500392
Overall Rank
556 | 96.27%
DOI
10.14778/1920841.1920844
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@article{bahmani_vldb11,
title = {{Fast Incremental and Personalized PageRank}},
author = {Bahmani, Bahman and Chowdhury, Abdur and Goel, Ashish},
journal = {PVLDB},
series = {{VLDB} '11},
volume = {4},
number = {3},
pages = {173--184},
doi = {10.14778/1920841.1920844},
url = {https://doi.org/10.14778/1920841.1920844},
year = {2011}
}
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Showing 28 of 28 citing papers.
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962
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3,896
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4,101
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4,777
Tornado: A System For Real-Time Iterative Analysis Over Evolving Data
2016
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6.4184698e-05
5,300
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2018
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5,914
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2023
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2018
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7,405
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2023
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SIGMOD
5.3432239e-05
9,947
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2025
SIGMOD
5.1038322e-05
10,835
Sankofa: Online Query-adaptive Dynamic Graph Summaries
2026
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4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
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