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Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme
Summary: Incremental PPR index-update for evolving graphs; fuses forward-push with a random-walk index for fast SSPPR. Achieves O(1) update time with compact structures; a sampling scheme removes vertex data while preserving updates and query quality.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h98638d61b797ebb9
Venue
SIGMOD
Year
2023
Pagerank
6.8626164e-05
Overall Rank
3,988 | 73.27%
DOI
10.1145/3588705
Incoming Non-self Citations Over Time
BibTeX Citation
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@inproceedings{hou_sigmod23,
title = {{Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme}},
author = {Hou, Guanhao and Guo, Qintian and Zhang, Fangyuan and Wang, Sibo and Wei, Zhewei},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588705},
url = {https://dl.acm.org/doi/10.1145/3588705},
year = {2023}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Cited Paper
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Pagerank
555
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2011
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2014
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2015
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Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank
2020
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BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart
2017
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