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Efficient Approximation of Kemeny’s Constant for Large Graphs

Summary: Scalable approximation of Kemeny’s constant for massive graphs via two Monte Carlo methods. RefinedMC trims redundant truncated random walks; ForestMC leverages a Laplacian-submatrix / forest-based identity for higher-accuracy estimation, avoiding explicit matrix inversion. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6961
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,172 | 23.36%
DOI
10.1145/3654937

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BibTeX Citation

@inproceedings{xia_sigmod24,
        title = {{Efficient Approximation of Kemeny’s Constant for Large Graphs}},
        author = {Xia, Haisong and Zhang, Zhongzhi},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654937},
        url = {https://dl.acm.org/doi/10.1145/3654937},
        year = {2024}
}

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