BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart
Summary: BePI uses block-elimination preprocessing and a preconditioned iterative solver to scale RWR on billion-edge graphs. Fast queries and low memory come from nonzero reduction and preconditioning, enabling 100x larger graphs with 130x less memory and 9x faster RWR. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jinhong Jung (Seoul National University)
- 2. Namyong Park (Seoul National University)
- 3. Lee Sael (State University of New York Korea)
- 4. U Kang (Seoul National University)
BibTeX Citation
@inproceedings{jung_sigmod17,
title = {{BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart}},
author = {Jung, Jinhong and Park, Namyong and Sael, Lee and Kang, U},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3035950},
url = {https://dl.acm.org/doi/10.1145/3035918.3035950},
year = {2017}
}
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