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Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs

Summary: Memory-aware framework for second-order random walks on billion-edge graphs, addressing memory blowups of conventional sampling with a cost model for node sampling. Acceptance-rejection sampling with per-node cost optimization allocates under a memory budget while minimizing time, delivering ~90% memory reduction and practical APIs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5837
Venue
SIGMOD
Year
2020
Pagerank
5.8813146e-05
Overall Rank
6,423 | 55.94%
DOI
10.1145/3318464.3380562

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{shao_sigmod20,
        title = {{Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs}},
        author = {Shao, Yingxia and Huang, Shiyue and Miao, Xupeng and Cui, Bin and Chen, Lei},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3380562},
        url = {https://dl.acm.org/doi/10.1145/3318464.3380562},
        year = {2020}
}

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